Overview

Dataset statistics

Number of variables54
Number of observations5128394
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 GiB
Average record size in memory432.0 B

Variable types

Numeric20
Categorical24
Text1
Boolean9

Alerts

numero_referencias has constant value ""Constant
CATEGORY has constant value ""Constant
Unnamed: 0 is highly overall correlated with cod_tiendaHigh correlation
cod_tienda is highly overall correlated with Unnamed: 0High correlation
cod_semana is highly overall correlated with month and 8 other fieldsHigh correlation
cod_producto is highly overall correlated with SEGMENTHigh correlation
ventas_unidades is highly overall correlated with ventas_valor and 1 other fieldsHigh correlation
ventas_valor is highly overall correlated with ventas_unidades and 1 other fieldsHigh correlation
ventas_volumen is highly overall correlated with ventas_unidades and 1 other fieldsHigh correlation
precio_real_unidades is highly overall correlated with precio_real_volumen and 2 other fieldsHigh correlation
precio_real_volumen is highly overall correlated with precio_real_unidades and 3 other fieldsHigh correlation
precio_tarifa_unidades is highly overall correlated with precio_real_unidades and 2 other fieldsHigh correlation
precio_tarifa_volumen is highly overall correlated with precio_real_unidades and 3 other fieldsHigh correlation
cod_provincia is highly overall correlated with Provincia and 1 other fieldsHigh correlation
postal_code is highly overall correlated with Provincia and 1 other fieldsHigh correlation
sales_surface_sqmeters is highly overall correlated with cod_canal and 2 other fieldsHigh correlation
Lat is highly overall correlated with Provincia and 1 other fieldsHigh correlation
Lon is highly overall correlated with Provincia and 1 other fieldsHigh correlation
TEMP_MINIMA is highly overall correlated with TEMP_MAXIMA and 2 other fieldsHigh correlation
TEMP_MAXIMA is highly overall correlated with TEMP_MINIMA and 2 other fieldsHigh correlation
TEMP_MEDIA is highly overall correlated with TEMP_MINIMA and 2 other fieldsHigh correlation
month is highly overall correlated with cod_semana and 1 other fieldsHigh correlation
year is highly overall correlated with cod_semana and 4 other fieldsHigh correlation
season is highly overall correlated with cod_semana and 4 other fieldsHigh correlation
SEGMENT is highly overall correlated with cod_productoHigh correlation
MANUFACTURER is highly overall correlated with BRANDHigh correlation
BRAND is highly overall correlated with MANUFACTURERHigh correlation
PACKAGING is highly overall correlated with VOLUMEHigh correlation
VOLUME is highly overall correlated with precio_real_volumen and 2 other fieldsHigh correlation
cod_canal is highly overall correlated with sales_surface_sqmeters and 2 other fieldsHigh correlation
Canal is highly overall correlated with sales_surface_sqmeters and 2 other fieldsHigh correlation
Channel is highly overall correlated with sales_surface_sqmeters and 2 other fieldsHigh correlation
Provincia is highly overall correlated with cod_provincia and 4 other fieldsHigh correlation
Comunidad autónoma is highly overall correlated with cod_provincia and 4 other fieldsHigh correlation
regional_holidays_2021 is highly overall correlated with cod_semana and 1 other fieldsHigh correlation
local_holidays_2021 is highly overall correlated with cod_semana and 2 other fieldsHigh correlation
regional_holidays_2022 is highly overall correlated with cod_semanaHigh correlation
local_holidays_2022 is highly overall correlated with cod_semana and 1 other fieldsHigh correlation
national_holidays_2023 is highly overall correlated with regional_holidays_2023High correlation
regional_holidays_2023 is highly overall correlated with cod_semana and 3 other fieldsHigh correlation
local_holidays_2023 is highly overall correlated with cod_semana and 2 other fieldsHigh correlation
VOLUME is highly imbalanced (59.1%)Imbalance
UNITS is highly imbalanced (60.4%)Imbalance
promocion_cabecera is highly imbalanced (80.8%)Imbalance
promocion_expositor is highly imbalanced (83.0%)Imbalance
promocion_extra_cantidad is highly imbalanced (98.4%)Imbalance
promocion_folleto is highly imbalanced (70.5%)Imbalance
promocion_isla is highly imbalanced (93.3%)Imbalance
promocion_multicompra is highly imbalanced (93.5%)Imbalance
promocion_regalo is highly imbalanced (95.0%)Imbalance
national_holidays_2021 is highly imbalanced (71.2%)Imbalance
national_holidays_2022 is highly imbalanced (66.9%)Imbalance
national_holidays_2023 is highly imbalanced (82.3%)Imbalance
regional_holidays_2023 is highly imbalanced (60.3%)Imbalance
local_holidays_2023 is highly imbalanced (51.1%)Imbalance
ventas_unidades is highly skewed (γ1 = 94.40156635)Skewed
ventas_valor is highly skewed (γ1 = 47.2030425)Skewed
ventas_volumen is highly skewed (γ1 = 50.84977748)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
ventas_volumen has 210240 (4.1%) zerosZeros
PRECIPITACION has 592889 (11.6%) zerosZeros

Reproduction

Analysis started2023-07-11 20:18:10.965093
Analysis finished2023-07-11 20:40:42.411722
Duration22 minutes and 31.45 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct5128394
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2564196.5
Minimum0
Maximum5128393
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.1 MiB
2023-07-11T22:40:42.547748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile256419.65
Q11282098.2
median2564196.5
Q33846294.8
95-th percentile4871973.3
Maximum5128393
Range5128393
Interquartile range (IQR)2564196.5

Descriptive statistics

Standard deviation1480440
Coefficient of variation (CV)0.57735044
Kurtosis-1.2
Mean2564196.5
Median Absolute Deviation (MAD)1282098.5
Skewness8.9009187 × 10-17
Sum1.315021 × 1013
Variance2.1917025 × 1012
MonotonicityStrictly increasing
2023-07-11T22:40:42.688773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
3418926 1
 
< 0.1%
3418933 1
 
< 0.1%
3418932 1
 
< 0.1%
3418931 1
 
< 0.1%
3418930 1
 
< 0.1%
3418929 1
 
< 0.1%
3418928 1
 
< 0.1%
3418927 1
 
< 0.1%
3418925 1
 
< 0.1%
Other values (5128384) 5128384
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
5128393 1
< 0.1%
5128392 1
< 0.1%
5128391 1
< 0.1%
5128390 1
< 0.1%
5128389 1
< 0.1%
5128388 1
< 0.1%
5128387 1
< 0.1%
5128386 1
< 0.1%
5128385 1
< 0.1%
5128384 1
< 0.1%

cod_tienda
Real number (ℝ)

HIGH CORRELATION 

Distinct543
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean289.33709
Minimum1
Maximum557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 MiB
2023-07-11T22:40:42.823827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31
Q1158
median282
Q3444
95-th percentile535
Maximum557
Range556
Interquartile range (IQR)286

Descriptive statistics

Standard deviation163.05702
Coefficient of variation (CV)0.56355382
Kurtosis-1.2054367
Mean289.33709
Median Absolute Deviation (MAD)140
Skewness-0.03004068
Sum1.4838346 × 109
Variance26587.592
MonotonicityNot monotonic
2023-07-11T22:40:42.945820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
228 39734
 
0.8%
196 38878
 
0.8%
302 38534
 
0.8%
16 37891
 
0.7%
207 37494
 
0.7%
50 37477
 
0.7%
483 37435
 
0.7%
336 37090
 
0.7%
173 36944
 
0.7%
391 36402
 
0.7%
Other values (533) 4750515
92.6%
ValueCountFrequency (%)
1 789
 
< 0.1%
2 1
 
< 0.1%
3 11024
0.2%
4 27134
0.5%
5 4484
 
0.1%
6 2886
 
0.1%
7 2385
 
< 0.1%
8 3344
 
0.1%
9 5980
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
557 3752
 
0.1%
556 6615
 
0.1%
555 6862
 
0.1%
554 10160
 
0.2%
553 31064
0.6%
552 46
 
< 0.1%
551 33237
0.6%
550 35083
0.7%
549 770
 
< 0.1%
548 8049
 
0.2%

cod_semana
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.157248
Minimum1
Maximum116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 MiB
2023-07-11T22:40:43.083875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q130
median60
Q388
95-th percentile111
Maximum116
Range115
Interquartile range (IQR)58

Descriptive statistics

Standard deviation33.362161
Coefficient of variation (CV)0.56395728
Kurtosis-1.1982501
Mean59.157248
Median Absolute Deviation (MAD)29
Skewness-0.020962079
Sum3.0338168 × 108
Variance1113.0338
MonotonicityNot monotonic
2023-07-11T22:40:43.218900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 50103
 
1.0%
101 47469
 
0.9%
48 46755
 
0.9%
80 46644
 
0.9%
71 46252
 
0.9%
79 46228
 
0.9%
74 46169
 
0.9%
99 46109
 
0.9%
73 46108
 
0.9%
78 46037
 
0.9%
Other values (106) 4660520
90.9%
ValueCountFrequency (%)
1 40472
0.8%
2 40170
0.8%
3 41358
0.8%
4 40949
0.8%
5 42308
0.8%
6 41966
0.8%
7 41838
0.8%
8 42214
0.8%
9 41693
0.8%
10 42570
0.8%
ValueCountFrequency (%)
116 45355
0.9%
115 45787
0.9%
114 45331
0.9%
113 45027
0.9%
112 45529
0.9%
111 44956
0.9%
110 44958
0.9%
109 44674
0.9%
108 44529
0.9%
107 43872
0.9%

cod_producto
Real number (ℝ)

HIGH CORRELATION 

Distinct1070
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean871.65905
Minimum4
Maximum1505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 MiB
2023-07-11T22:40:43.358925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile222
Q1516
median927
Q31229
95-th percentile1463
Maximum1505
Range1501
Interquartile range (IQR)713

Descriptive statistics

Standard deviation406.95303
Coefficient of variation (CV)0.4668718
Kurtosis-1.1131356
Mean871.65905
Median Absolute Deviation (MAD)330
Skewness-0.2243403
Sum4.470211 × 109
Variance165610.77
MonotonicityNot monotonic
2023-07-11T22:40:43.490919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
309 41752
 
0.8%
661 40286
 
0.8%
1132 40037
 
0.8%
1033 39523
 
0.8%
1500 38251
 
0.7%
1312 37435
 
0.7%
1120 35938
 
0.7%
654 35805
 
0.7%
1502 34429
 
0.7%
1075 34182
 
0.7%
Other values (1060) 4750756
92.6%
ValueCountFrequency (%)
4 152
 
< 0.1%
5 118
 
< 0.1%
6 4
 
< 0.1%
7 17
 
< 0.1%
8 117
 
< 0.1%
9 711
 
< 0.1%
10 6184
0.1%
12 61
 
< 0.1%
13 62
 
< 0.1%
15 198
 
< 0.1%
ValueCountFrequency (%)
1505 2869
 
0.1%
1504 8909
 
0.2%
1503 10373
 
0.2%
1502 34429
0.7%
1501 11551
 
0.2%
1500 38251
0.7%
1499 578
 
< 0.1%
1498 1055
 
< 0.1%
1497 1497
 
< 0.1%
1496 24745
0.5%

ventas_unidades
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7610
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.811348
Minimum0
Maximum174864
Zeros1654
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.1 MiB
2023-07-11T22:40:43.644947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median9
Q327
95-th percentile146
Maximum174864
Range174864
Interquartile range (IQR)24

Descriptive statistics

Standard deviation525.63532
Coefficient of variation (CV)10.768711
Kurtosis15594.08
Mean48.811348
Median Absolute Deviation (MAD)7
Skewness94.401566
Sum2.5032382 × 108
Variance276292.49
MonotonicityNot monotonic
2023-07-11T22:40:43.781973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 548152
 
10.7%
2 479746
 
9.4%
3 325918
 
6.4%
4 298007
 
5.8%
6 290526
 
5.7%
5 210167
 
4.1%
8 188550
 
3.7%
7 170644
 
3.3%
12 143653
 
2.8%
9 130558
 
2.5%
Other values (7600) 2342473
45.7%
ValueCountFrequency (%)
0 1654
 
< 0.1%
1 548152
10.7%
2 479746
9.4%
3 325918
6.4%
4 298007
5.8%
5 210167
 
4.1%
6 290526
5.7%
7 170644
 
3.3%
8 188550
 
3.7%
9 130558
 
2.5%
ValueCountFrequency (%)
174864 1
< 0.1%
164226 1
< 0.1%
151243 1
< 0.1%
128472 1
< 0.1%
120635 1
< 0.1%
117422 1
< 0.1%
109495 1
< 0.1%
105834 1
< 0.1%
102165 1
< 0.1%
101430 1
< 0.1%

ventas_valor
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct74967
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.40386
Minimum0.02
Maximum68038.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 MiB
2023-07-11T22:40:43.925028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile1.48
Q15.12
median12.6
Q332.45
95-th percentile164.32
Maximum68038.65
Range68038.63
Interquartile range (IQR)27.33

Descriptive statistics

Standard deviation226.74559
Coefficient of variation (CV)4.7832726
Kurtosis5899.8508
Mean47.40386
Median Absolute Deviation (MAD)9.3
Skewness47.203042
Sum2.4310567 × 108
Variance51413.562
MonotonicityNot monotonic
2023-07-11T22:40:44.056023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 19933
 
0.4%
3 16108
 
0.3%
9 14982
 
0.3%
12 13659
 
0.3%
4.5 13629
 
0.3%
2 11604
 
0.2%
7.2 11304
 
0.2%
4.8 11252
 
0.2%
6.6 11020
 
0.2%
18 10700
 
0.2%
Other values (74957) 4994203
97.4%
ValueCountFrequency (%)
0.02 1
 
< 0.1%
0.05 1
 
< 0.1%
0.07 1
 
< 0.1%
0.09 1
 
< 0.1%
0.11 1
 
< 0.1%
0.13 3
 
< 0.1%
0.15 18
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.18 2
 
< 0.1%
ValueCountFrequency (%)
68038.65 1
< 0.1%
56554.58 1
< 0.1%
43725.44 1
< 0.1%
42439.54 1
< 0.1%
40658.7 1
< 0.1%
38094.9 1
< 0.1%
37595.25 1
< 0.1%
36982.53 1
< 0.1%
36374 1
< 0.1%
35929.76 1
< 0.1%

ventas_volumen
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct5674
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.956063
Minimum0
Maximum57705
Zeros210240
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size39.1 MiB
2023-07-11T22:40:44.197081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median6
Q320
95-th percentile122
Maximum57705
Range57705
Interquartile range (IQR)18

Descriptive statistics

Standard deviation250.1391
Coefficient of variation (CV)6.7685539
Kurtosis5204.6165
Mean36.956063
Median Absolute Deviation (MAD)5
Skewness50.849777
Sum1.8952525 × 108
Variance62569.571
MonotonicityNot monotonic
2023-07-11T22:40:44.327071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 646173
 
12.6%
2 561030
 
10.9%
3 410278
 
8.0%
4 293284
 
5.7%
5 256287
 
5.0%
6 240402
 
4.7%
0 210240
 
4.1%
8 204109
 
4.0%
9 137867
 
2.7%
12 135944
 
2.7%
Other values (5664) 2032780
39.6%
ValueCountFrequency (%)
0 210240
 
4.1%
1 646173
12.6%
2 561030
10.9%
3 410278
8.0%
4 293284
5.7%
5 256287
 
5.0%
6 240402
 
4.7%
7 120341
 
2.3%
8 204109
 
4.0%
9 137867
 
2.7%
ValueCountFrequency (%)
57705 1
< 0.1%
54195 1
< 0.1%
49910 1
< 0.1%
49191 1
< 0.1%
42396 1
< 0.1%
41996 1
< 0.1%
39810 1
< 0.1%
38749 1
< 0.1%
37487 1
< 0.1%
36133 1
< 0.1%

numero_referencias
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
1
5128394 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5128394
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5128394
100.0%

Length

2023-07-11T22:40:44.453093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T22:40:44.571145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 5128394
100.0%

Most occurring characters

ValueCountFrequency (%)
1 5128394
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5128394
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5128394
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5128394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5128394
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5128394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5128394
100.0%

precio_real_unidades
Real number (ℝ)

HIGH CORRELATION 

Distinct1886
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1427971
Minimum0.01
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 MiB
2023-07-11T22:40:44.670159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.38
Q10.62
median1.21
Q32.6
95-th percentile6.95
Maximum139
Range138.99
Interquartile range (IQR)1.98

Descriptive statistics

Standard deviation2.4113312
Coefficient of variation (CV)1.1253194
Kurtosis14.662808
Mean2.1427971
Median Absolute Deviation (MAD)0.68
Skewness2.6565171
Sum10989108
Variance5.8145179
MonotonicityNot monotonic
2023-07-11T22:40:44.792186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.53 83741
 
1.6%
0.52 72501
 
1.4%
0.55 69798
 
1.4%
0.5 68655
 
1.3%
0.59 68154
 
1.3%
1 59269
 
1.2%
0.51 58126
 
1.1%
0.49 52493
 
1.0%
0.75 51920
 
1.0%
0.54 51670
 
1.0%
Other values (1876) 4492067
87.6%
ValueCountFrequency (%)
0.01 1
 
< 0.1%
0.03 5
 
< 0.1%
0.04 1
 
< 0.1%
0.05 6
 
< 0.1%
0.06 8
 
< 0.1%
0.07 10
< 0.1%
0.08 9
< 0.1%
0.09 19
< 0.1%
0.1 22
< 0.1%
0.11 21
< 0.1%
ValueCountFrequency (%)
139 1
 
< 0.1%
101.25 1
 
< 0.1%
99.04 1
 
< 0.1%
98.29 1
 
< 0.1%
93 2
 
< 0.1%
87.76 1
 
< 0.1%
87.71 1
 
< 0.1%
83.15 1
 
< 0.1%
55.04 25
< 0.1%
49 8
 
< 0.1%

precio_real_volumen
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1610656
Minimum0.2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 MiB
2023-07-11T22:40:44.948184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.33
Q10.33
median0.36
Q31.5
95-th percentile3.96
Maximum50
Range49.8
Interquartile range (IQR)1.17

Descriptive statistics

Standard deviation1.5235775
Coefficient of variation (CV)1.3122234
Kurtosis13.411904
Mean1.1610656
Median Absolute Deviation (MAD)0.06
Skewness2.8339742
Sum5954402
Variance2.3212885
MonotonicityNot monotonic
2023-07-11T22:40:45.110727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.33 2456975
47.9%
1.5 612194
 
11.9%
0.5 534833
 
10.4%
3.96 294290
 
5.7%
1 188479
 
3.7%
1.98 129824
 
2.5%
3 121978
 
2.4%
0.75 76859
 
1.5%
6 70423
 
1.4%
1.32 70422
 
1.4%
Other values (57) 572117
 
11.2%
ValueCountFrequency (%)
0.2 422
 
< 0.1%
0.25 54158
 
1.1%
0.3 17254
 
0.3%
0.33 2456975
47.9%
0.36 67844
 
1.3%
0.38 27933
 
0.5%
0.44 21146
 
0.4%
0.45 4783
 
0.1%
0.47 1581
 
< 0.1%
0.5 534833
 
10.4%
ValueCountFrequency (%)
50 25
 
< 0.1%
20 25
 
< 0.1%
12 34
 
< 0.1%
9.24 30139
0.6%
9 5475
 
0.1%
8.4 100
 
< 0.1%
7.92 40960
0.8%
7 4
 
< 0.1%
6.75 265
 
< 0.1%
6.6 707
 
< 0.1%

precio_tarifa_unidades
Real number (ℝ)

HIGH CORRELATION 

Distinct1958
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2455012
Minimum0.01
Maximum140.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 MiB
2023-07-11T22:40:45.266095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.42
Q10.65
median1.28
Q32.66
95-th percentile7.22
Maximum140.85
Range140.84
Interquartile range (IQR)2.01

Descriptive statistics

Standard deviation2.6147882
Coefficient of variation (CV)1.1644564
Kurtosis164.00515
Mean2.2455012
Median Absolute Deviation (MAD)0.72
Skewness5.5705333
Sum11515815
Variance6.8371174
MonotonicityNot monotonic
2023-07-11T22:40:45.398746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.53 97976
 
1.9%
0.52 79426
 
1.5%
0.59 78976
 
1.5%
0.55 78858
 
1.5%
0.75 60446
 
1.2%
0.51 59183
 
1.2%
0.54 55844
 
1.1%
0.5 54109
 
1.1%
1.29 52712
 
1.0%
0.61 52102
 
1.0%
Other values (1948) 4458762
86.9%
ValueCountFrequency (%)
0.01 1
 
< 0.1%
0.08 1
 
< 0.1%
0.09 2
 
< 0.1%
0.1 5
 
< 0.1%
0.11 3
 
< 0.1%
0.12 3
 
< 0.1%
0.13 7
 
< 0.1%
0.14 16
 
< 0.1%
0.15 93
< 0.1%
0.16 18
 
< 0.1%
ValueCountFrequency (%)
140.85 1
 
< 0.1%
140.83 1
 
< 0.1%
140.77 1
 
< 0.1%
139 1
 
< 0.1%
126.86 1
 
< 0.1%
126.82 1
 
< 0.1%
126.81 1
 
< 0.1%
126.75 1
 
< 0.1%
126.74 4
< 0.1%
126.73 4
< 0.1%

precio_tarifa_volumen
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1610656
Minimum0.2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 MiB
2023-07-11T22:40:45.536839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.33
Q10.33
median0.36
Q31.5
95-th percentile3.96
Maximum50
Range49.8
Interquartile range (IQR)1.17

Descriptive statistics

Standard deviation1.5235775
Coefficient of variation (CV)1.3122235
Kurtosis13.411904
Mean1.1610656
Median Absolute Deviation (MAD)0.06
Skewness2.8339743
Sum5954401.6
Variance2.3212885
MonotonicityNot monotonic
2023-07-11T22:40:45.671861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.33 2456975
47.9%
1.5 612194
 
11.9%
0.5 534833
 
10.4%
3.96 294290
 
5.7%
1 188480
 
3.7%
1.98 129824
 
2.5%
3 121978
 
2.4%
0.75 76859
 
1.5%
6 70423
 
1.4%
1.32 70422
 
1.4%
Other values (57) 572116
 
11.2%
ValueCountFrequency (%)
0.2 422
 
< 0.1%
0.25 54158
 
1.1%
0.3 17254
 
0.3%
0.33 2456975
47.9%
0.36 67844
 
1.3%
0.38 27933
 
0.5%
0.44 21146
 
0.4%
0.45 4783
 
0.1%
0.47 1581
 
< 0.1%
0.5 534833
 
10.4%
ValueCountFrequency (%)
50 25
 
< 0.1%
20 25
 
< 0.1%
12 34
 
< 0.1%
9.24 30139
0.6%
9 5475
 
0.1%
8.4 100
 
< 0.1%
7.92 40960
0.8%
7 4
 
< 0.1%
6.75 265
 
< 0.1%
6.6 707
 
< 0.1%

month
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
January
641353 
February
524613 
March
482790 
May
450933 
October
438345 
Other values (7)
2590360 

Length

Max length9
Median length7
Mean length6.174111
Min length3

Characters and Unicode

Total characters31663274
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOctober
2nd rowOctober
3rd rowOctober
4th rowOctober
5th rowOctober

Common Values

ValueCountFrequency (%)
January 641353
12.5%
February 524613
10.2%
March 482790
9.4%
May 450933
8.8%
October 438345
8.5%
July 408225
8.0%
August 398822
7.8%
November 368724
7.2%
June 364448
7.1%
September 351929
6.9%
Other values (2) 698212
13.6%

Length

2023-07-11T22:40:45.789854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
january 641353
12.5%
february 524613
10.2%
march 482790
9.4%
may 450933
8.8%
october 438345
8.5%
july 408225
8.0%
august 398822
7.8%
november 368724
7.2%
june 364448
7.1%
september 351929
6.9%
Other values (2) 698212
13.6%

Most occurring characters

ValueCountFrequency (%)
e 4170539
13.2%
r 4030579
12.7%
a 2741042
 
8.7%
u 2736283
 
8.6%
b 2033577
 
6.4%
y 2025124
 
6.4%
J 1414026
 
4.5%
c 1271101
 
4.0%
t 1189096
 
3.8%
m 1070619
 
3.4%
Other values (16) 8981288
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26534880
83.8%
Uppercase Letter 5128394
 
16.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4170539
15.7%
r 4030579
15.2%
a 2741042
10.3%
u 2736283
10.3%
b 2033577
7.7%
y 2025124
7.6%
c 1271101
 
4.8%
t 1189096
 
4.5%
m 1070619
 
4.0%
n 1005801
 
3.8%
Other values (8) 4261119
16.1%
Uppercase Letter
ValueCountFrequency (%)
J 1414026
27.6%
M 933723
18.2%
A 747068
14.6%
F 524613
 
10.2%
O 438345
 
8.5%
N 368724
 
7.2%
S 351929
 
6.9%
D 349966
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 31663274
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4170539
13.2%
r 4030579
12.7%
a 2741042
 
8.7%
u 2736283
 
8.6%
b 2033577
 
6.4%
y 2025124
 
6.4%
J 1414026
 
4.5%
c 1271101
 
4.0%
t 1189096
 
3.8%
m 1070619
 
3.4%
Other values (16) 8981288
28.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31663274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4170539
13.2%
r 4030579
12.7%
a 2741042
 
8.7%
u 2736283
 
8.6%
b 2033577
 
6.4%
y 2025124
 
6.4%
J 1414026
 
4.5%
c 1271101
 
4.0%
t 1189096
 
3.8%
m 1070619
 
3.4%
Other values (16) 8981288
28.4%

year
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
2022
2333101 
2021
2257303 
2023
537990 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters20513576
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2022 2333101
45.5%
2021 2257303
44.0%
2023 537990
 
10.5%

Length

2023-07-11T22:40:45.894933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T22:40:46.003898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2022 2333101
45.5%
2021 2257303
44.0%
2023 537990
 
10.5%

Most occurring characters

ValueCountFrequency (%)
2 12589889
61.4%
0 5128394
25.0%
1 2257303
 
11.0%
3 537990
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20513576
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 12589889
61.4%
0 5128394
25.0%
1 2257303
 
11.0%
3 537990
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 20513576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 12589889
61.4%
0 5128394
25.0%
1 2257303
 
11.0%
3 537990
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20513576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 12589889
61.4%
0 5128394
25.0%
1 2257303
 
11.0%
3 537990
 
2.6%

season
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
Winter
1515932 
Spring
1281969 
Summer
1171495 
Fall
1158998 

Length

Max length6
Median length6
Mean length5.5480074
Min length4

Characters and Unicode

Total characters28452368
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFall
2nd rowFall
3rd rowFall
4th rowFall
5th rowFall

Common Values

ValueCountFrequency (%)
Winter 1515932
29.6%
Spring 1281969
25.0%
Summer 1171495
22.8%
Fall 1158998
22.6%

Length

2023-07-11T22:40:46.113944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T22:40:46.237956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
winter 1515932
29.6%
spring 1281969
25.0%
summer 1171495
22.8%
fall 1158998
22.6%

Most occurring characters

ValueCountFrequency (%)
r 3969396
14.0%
i 2797901
9.8%
n 2797901
9.8%
e 2687427
9.4%
S 2453464
8.6%
m 2342990
8.2%
l 2317996
8.1%
W 1515932
 
5.3%
t 1515932
 
5.3%
p 1281969
 
4.5%
Other values (4) 4771460
16.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23323974
82.0%
Uppercase Letter 5128394
 
18.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 3969396
17.0%
i 2797901
12.0%
n 2797901
12.0%
e 2687427
11.5%
m 2342990
10.0%
l 2317996
9.9%
t 1515932
 
6.5%
p 1281969
 
5.5%
g 1281969
 
5.5%
u 1171495
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
S 2453464
47.8%
W 1515932
29.6%
F 1158998
22.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 28452368
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 3969396
14.0%
i 2797901
9.8%
n 2797901
9.8%
e 2687427
9.4%
S 2453464
8.6%
m 2342990
8.2%
l 2317996
8.1%
W 1515932
 
5.3%
t 1515932
 
5.3%
p 1281969
 
4.5%
Other values (4) 4771460
16.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28452368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 3969396
14.0%
i 2797901
9.8%
n 2797901
9.8%
e 2687427
9.4%
S 2453464
8.6%
m 2342990
8.2%
l 2317996
8.1%
W 1515932
 
5.3%
t 1515932
 
5.3%
p 1281969
 
4.5%
Other values (4) 4771460
16.8%

CATEGORY
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
BEER
5128394 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters20513576
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBEER
2nd rowBEER
3rd rowBEER
4th rowBEER
5th rowBEER

Common Values

ValueCountFrequency (%)
BEER 5128394
100.0%

Length

2023-07-11T22:40:46.336996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T22:40:46.434002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
beer 5128394
100.0%

Most occurring characters

ValueCountFrequency (%)
E 10256788
50.0%
B 5128394
25.0%
R 5128394
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 20513576
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 10256788
50.0%
B 5128394
25.0%
R 5128394
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20513576
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 10256788
50.0%
B 5128394
25.0%
R 5128394
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20513576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 10256788
50.0%
B 5128394
25.0%
R 5128394
25.0%

SEGMENT
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
NATIONAL
1725496 
IMPORT PREMIUM
726470 
IMPORT SPECIAL
597377 
FLAVOURED
505882 
EXTRA
503921 
Other values (5)
1069248 

Length

Max length15
Median length14
Mean length9.8182035
Min length5

Characters and Unicode

Total characters50351616
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEXTRA
2nd rowEXTRA
3rd rowEXTRA
4th rowEXTRA
5th rowEXTRA

Common Values

ValueCountFrequency (%)
NATIONAL 1725496
33.6%
IMPORT PREMIUM 726470
14.2%
IMPORT SPECIAL 597377
 
11.6%
FLAVOURED 505882
 
9.9%
EXTRA 503921
 
9.8%
ZERO ALCOHOL 353559
 
6.9%
WITHOUT ALCOHOL 251107
 
4.9%
BLACK 227526
 
4.4%
ARTISANS 134912
 
2.6%
CELIACS 102144
 
2.0%

Length

2023-07-11T22:40:46.518688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T22:40:46.650515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
national 1725496
24.5%
import 1323847
18.8%
premium 726470
10.3%
alcohol 604666
 
8.6%
special 597377
 
8.5%
flavoured 505882
 
7.2%
extra 503921
 
7.1%
zero 353559
 
5.0%
without 251107
 
3.6%
black 227526
 
3.2%
Other values (2) 237056
 
3.4%

Most occurring characters

ValueCountFrequency (%)
A 6262332
12.4%
O 5369223
10.7%
I 4861353
9.7%
L 4367757
8.7%
T 4190390
8.3%
N 3585904
 
7.1%
R 3548591
 
7.0%
E 2789353
 
5.5%
M 2776787
 
5.5%
P 2647694
 
5.3%
Other values (13) 9952232
19.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 48423103
96.2%
Space Separator 1928513
 
3.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 6262332
12.9%
O 5369223
11.1%
I 4861353
10.0%
L 4367757
9.0%
T 4190390
8.7%
N 3585904
7.4%
R 3548591
7.3%
E 2789353
 
5.8%
M 2776787
 
5.7%
P 2647694
 
5.5%
Other values (12) 8023719
16.6%
Space Separator
ValueCountFrequency (%)
1928513
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 48423103
96.2%
Common 1928513
 
3.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 6262332
12.9%
O 5369223
11.1%
I 4861353
10.0%
L 4367757
9.0%
T 4190390
8.7%
N 3585904
7.4%
R 3548591
7.3%
E 2789353
 
5.8%
M 2776787
 
5.7%
P 2647694
 
5.5%
Other values (12) 8023719
16.6%
Common
ValueCountFrequency (%)
1928513
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50351616
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 6262332
12.4%
O 5369223
10.7%
I 4861353
9.7%
L 4367757
8.7%
T 4190390
8.3%
N 3585904
 
7.1%
R 3548591
 
7.0%
E 2789353
 
5.5%
M 2776787
 
5.5%
P 2647694
 
5.3%
Other values (13) 9952232
19.8%

MANUFACTURER
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
MANUFACTURER 3
1528162 
MANUFACTURER 2
1213383 
OTHER MANUFACTURERS
985817 
MANUFACTURER 1
539077 
MANUFACTURER 6
432471 
Other values (2)
429484 

Length

Max length19
Median length14
Mean length14.961136
Min length14

Characters and Unicode

Total characters76726601
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMANUFACTURER 2
2nd rowMANUFACTURER 2
3rd rowMANUFACTURER 2
4th rowMANUFACTURER 4
5th rowMANUFACTURER 4

Common Values

ValueCountFrequency (%)
MANUFACTURER 3 1528162
29.8%
MANUFACTURER 2 1213383
23.7%
OTHER MANUFACTURERS 985817
19.2%
MANUFACTURER 1 539077
 
10.5%
MANUFACTURER 6 432471
 
8.4%
MANUFACTURER 4 220063
 
4.3%
MANUFACTURER 5 209421
 
4.1%

Length

2023-07-11T22:40:46.781509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T22:40:46.904597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
manufacturer 4142577
40.4%
3 1528162
 
14.9%
2 1213383
 
11.8%
other 985817
 
9.6%
manufacturers 985817
 
9.6%
1 539077
 
5.3%
6 432471
 
4.2%
4 220063
 
2.1%
5 209421
 
2.0%

Most occurring characters

ValueCountFrequency (%)
R 11242605
14.7%
A 10256788
13.4%
U 10256788
13.4%
T 6114211
8.0%
E 6114211
8.0%
M 5128394
6.7%
5128394
6.7%
C 5128394
6.7%
F 5128394
6.7%
N 5128394
6.7%
Other values (9) 7100028
9.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 67455630
87.9%
Space Separator 5128394
 
6.7%
Decimal Number 4142577
 
5.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 11242605
16.7%
A 10256788
15.2%
U 10256788
15.2%
T 6114211
9.1%
E 6114211
9.1%
M 5128394
7.6%
C 5128394
7.6%
F 5128394
7.6%
N 5128394
7.6%
O 985817
 
1.5%
Other values (2) 1971634
 
2.9%
Decimal Number
ValueCountFrequency (%)
3 1528162
36.9%
2 1213383
29.3%
1 539077
 
13.0%
6 432471
 
10.4%
4 220063
 
5.3%
5 209421
 
5.1%
Space Separator
ValueCountFrequency (%)
5128394
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 67455630
87.9%
Common 9270971
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 11242605
16.7%
A 10256788
15.2%
U 10256788
15.2%
T 6114211
9.1%
E 6114211
9.1%
M 5128394
7.6%
C 5128394
7.6%
F 5128394
7.6%
N 5128394
7.6%
O 985817
 
1.5%
Other values (2) 1971634
 
2.9%
Common
ValueCountFrequency (%)
5128394
55.3%
3 1528162
 
16.5%
2 1213383
 
13.1%
1 539077
 
5.8%
6 432471
 
4.7%
4 220063
 
2.4%
5 209421
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 76726601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 11242605
14.7%
A 10256788
13.4%
U 10256788
13.4%
T 6114211
8.0%
E 6114211
8.0%
M 5128394
6.7%
5128394
6.7%
C 5128394
6.7%
F 5128394
6.7%
N 5128394
6.7%
Other values (9) 7100028
9.3%

BRAND
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
OTHER MAN - OTHER BRANDS
985817 
MAN 3 - BRAND 3
580241 
MAN 3 - BRAND 4
543439 
MAN 6 - BRAND 1
432471 
MAN 1 - BRAND 1
399553 
Other values (16)
2186873 

Length

Max length24
Median length15
Mean length17.267193
Min length15

Characters and Unicode

Total characters88552968
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMAN 2 - BRAND 1
2nd rowMAN 2 - BRAND 1
3rd rowMAN 2 - BRAND 2
4th rowMAN 4 - BRAND 1
5th rowMAN 4 - BRAND 1

Common Values

ValueCountFrequency (%)
OTHER MAN - OTHER BRANDS 985817
19.2%
MAN 3 - BRAND 3 580241
11.3%
MAN 3 - BRAND 4 543439
10.6%
MAN 6 - BRAND 1 432471
8.4%
MAN 1 - BRAND 1 399553
7.8%
MAN 2 - BRAND 1 330638
 
6.4%
MAN 2 - BRAND 2 297584
 
5.8%
MAN 2 - OTHER BRANDS 262225
 
5.1%
MAN 4 - BRAND 1 220063
 
4.3%
MAN 3 - BRAND 1 213625
 
4.2%
Other values (11) 862738
16.8%

Length

2023-07-11T22:40:47.029588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
man 5128394
20.0%
5128394
20.0%
brand 3591636
14.0%
other 2522575
9.8%
3 2305746
9.0%
1 2178727
8.5%
2 1688372
 
6.6%
brands 1536758
 
6.0%
4 809477
 
3.2%
6 432471
 
1.7%

Most occurring characters

ValueCountFrequency (%)
20513576
23.2%
A 10256788
11.6%
N 10256788
11.6%
R 7650969
 
8.6%
- 5128394
 
5.8%
D 5128394
 
5.8%
B 5128394
 
5.8%
M 5128394
 
5.8%
T 2522575
 
2.8%
O 2522575
 
2.8%
Other values (9) 14316121
16.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 55176785
62.3%
Space Separator 20513576
 
23.2%
Decimal Number 7734213
 
8.7%
Dash Punctuation 5128394
 
5.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 10256788
18.6%
N 10256788
18.6%
R 7650969
13.9%
D 5128394
9.3%
B 5128394
9.3%
M 5128394
9.3%
T 2522575
 
4.6%
O 2522575
 
4.6%
E 2522575
 
4.6%
H 2522575
 
4.6%
Decimal Number
ValueCountFrequency (%)
3 2305746
29.8%
1 2178727
28.2%
2 1688372
21.8%
4 809477
 
10.5%
6 432471
 
5.6%
5 319420
 
4.1%
Space Separator
ValueCountFrequency (%)
20513576
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5128394
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55176785
62.3%
Common 33376183
37.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 10256788
18.6%
N 10256788
18.6%
R 7650969
13.9%
D 5128394
9.3%
B 5128394
9.3%
M 5128394
9.3%
T 2522575
 
4.6%
O 2522575
 
4.6%
E 2522575
 
4.6%
H 2522575
 
4.6%
Common
ValueCountFrequency (%)
20513576
61.5%
- 5128394
 
15.4%
3 2305746
 
6.9%
1 2178727
 
6.5%
2 1688372
 
5.1%
4 809477
 
2.4%
6 432471
 
1.3%
5 319420
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88552968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20513576
23.2%
A 10256788
11.6%
N 10256788
11.6%
R 7650969
 
8.6%
- 5128394
 
5.8%
D 5128394
 
5.8%
B 5128394
 
5.8%
M 5128394
 
5.8%
T 2522575
 
2.8%
O 2522575
 
2.8%
Other values (9) 14316121
16.2%

PACKAGING
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
CRISTAL BOTTLE
2800764 
CAN
2283943 
PET
 
26125
OTHER
 
17562

Length

Max length14
Median length14
Mean length9.0142665
Min length3

Characters and Unicode

Total characters46228710
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCAN
2nd rowCRISTAL BOTTLE
3rd rowCRISTAL BOTTLE
4th rowCRISTAL BOTTLE
5th rowCRISTAL BOTTLE

Common Values

ValueCountFrequency (%)
CRISTAL BOTTLE 2800764
54.6%
CAN 2283943
44.5%
PET 26125
 
0.5%
OTHER 17562
 
0.3%

Length

2023-07-11T22:40:47.125390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T22:40:47.666141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
cristal 2800764
35.3%
bottle 2800764
35.3%
can 2283943
28.8%
pet 26125
 
0.3%
other 17562
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T 8445979
18.3%
L 5601528
12.1%
C 5084707
11.0%
A 5084707
11.0%
E 2844451
 
6.2%
R 2818326
 
6.1%
O 2818326
 
6.1%
I 2800764
 
6.1%
S 2800764
 
6.1%
2800764
 
6.1%
Other values (4) 5128394
11.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 43427946
93.9%
Space Separator 2800764
 
6.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 8445979
19.4%
L 5601528
12.9%
C 5084707
11.7%
A 5084707
11.7%
E 2844451
 
6.5%
R 2818326
 
6.5%
O 2818326
 
6.5%
I 2800764
 
6.4%
S 2800764
 
6.4%
B 2800764
 
6.4%
Other values (3) 2327630
 
5.4%
Space Separator
ValueCountFrequency (%)
2800764
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 43427946
93.9%
Common 2800764
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 8445979
19.4%
L 5601528
12.9%
C 5084707
11.7%
A 5084707
11.7%
E 2844451
 
6.5%
R 2818326
 
6.5%
O 2818326
 
6.5%
I 2800764
 
6.4%
S 2800764
 
6.4%
B 2800764
 
6.4%
Other values (3) 2327630
 
5.4%
Common
ValueCountFrequency (%)
2800764
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46228710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 8445979
18.3%
L 5601528
12.1%
C 5084707
11.0%
A 5084707
11.0%
E 2844451
 
6.2%
R 2818326
 
6.1%
O 2818326
 
6.1%
I 2800764
 
6.1%
S 2800764
 
6.1%
2800764
 
6.1%
Other values (4) 5128394
11.1%

VOLUME
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
330ML
3128367 
250ML
847448 
500ML
544150 
1000ML
 
196210
355ML
 
85973
Other values (23)
326246 

Length

Max length35
Median length35
Mean length35
Min length35

Characters and Unicode

Total characters179493790
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row330ML
2nd row330ML
3rd row330ML
4th row330ML
5th row330ML

Common Values

ValueCountFrequency (%)
330ML 3128367
61.0%
250ML 847448
 
16.5%
500ML 544150
 
10.6%
1000ML 196210
 
3.8%
355ML 85973
 
1.7%
750ML 79527
 
1.6%
375ML 47077
 
0.9%
200ML 32212
 
0.6%
440ML 22555
 
0.4%
568ML 21856
 
0.4%
Other values (18) 123019
 
2.4%

Length

2023-07-11T22:40:47.760202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
330ml 3128367
61.0%
250ml 847448
 
16.5%
500ml 544150
 
10.6%
1000ml 196210
 
3.8%
355ml 85973
 
1.7%
750ml 79527
 
1.6%
375ml 47077
 
0.9%
200ml 32212
 
0.6%
440ml 22555
 
0.4%
568ml 21856
 
0.4%
Other values (18) 123019
 
2.4%

Most occurring characters

ValueCountFrequency (%)
153619173
85.6%
3 6409479
 
3.6%
0 6002856
 
3.3%
M 5128394
 
2.9%
L 5128394
 
2.9%
5 1765157
 
1.0%
2 906353
 
0.5%
1 248546
 
0.1%
7 129592
 
0.1%
6 75074
 
< 0.1%
Other values (3) 80772
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 153619173
85.6%
Decimal Number 15617829
 
8.7%
Uppercase Letter 10256788
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 6409479
41.0%
0 6002856
38.4%
5 1765157
 
11.3%
2 906353
 
5.8%
1 248546
 
1.6%
7 129592
 
0.8%
6 75074
 
0.5%
4 51474
 
0.3%
8 29006
 
0.2%
9 292
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
M 5128394
50.0%
L 5128394
50.0%
Space Separator
ValueCountFrequency (%)
153619173
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 169237002
94.3%
Latin 10256788
 
5.7%

Most frequent character per script

Common
ValueCountFrequency (%)
153619173
90.8%
3 6409479
 
3.8%
0 6002856
 
3.5%
5 1765157
 
1.0%
2 906353
 
0.5%
1 248546
 
0.1%
7 129592
 
0.1%
6 75074
 
< 0.1%
4 51474
 
< 0.1%
8 29006
 
< 0.1%
Latin
ValueCountFrequency (%)
M 5128394
50.0%
L 5128394
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 179493790
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
153619173
85.6%
3 6409479
 
3.6%
0 6002856
 
3.3%
M 5128394
 
2.9%
L 5128394
 
2.9%
5 1765157
 
1.0%
2 906353
 
0.5%
1 248546
 
0.1%
7 129592
 
0.1%
6 75074
 
< 0.1%
Other values (3) 80772
 
< 0.1%

UNITS
Categorical

IMBALANCE 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
1CT
3547432 
6CT
795273 
12CT
432904 
24CT
 
109088
4CT
 
73156
Other values (10)
 
170541

Length

Max length35
Median length35
Mean length35
Min length35

Characters and Unicode

Total characters179493790
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1CT
2nd row1CT
3rd row6CT
4th row6CT
5th row6CT

Common Values

ValueCountFrequency (%)
1CT 3547432
69.2%
6CT 795273
 
15.5%
12CT 432904
 
8.4%
24CT 109088
 
2.1%
4CT 73156
 
1.4%
8CT 55648
 
1.1%
28CT 30143
 
0.6%
10CT 25685
 
0.5%
9CT 16085
 
0.3%
3CT 14465
 
0.3%
Other values (5) 28515
 
0.6%

Length

2023-07-11T22:40:47.857175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1ct 3547432
69.2%
6ct 795273
 
15.5%
12ct 432904
 
8.4%
24ct 109088
 
2.1%
4ct 73156
 
1.4%
8ct 55648
 
1.1%
28ct 30143
 
0.6%
10ct 25685
 
0.5%
9ct 16085
 
0.3%
3ct 14465
 
0.3%
Other values (5) 28515
 
0.6%

Most occurring characters

ValueCountFrequency (%)
163492230
91.1%
C 5128394
 
2.9%
T 5128394
 
2.9%
1 4018989
 
2.2%
6 796215
 
0.4%
2 580732
 
0.3%
4 182244
 
0.1%
8 97817
 
0.1%
0 31275
 
< 0.1%
9 16085
 
< 0.1%
Other values (2) 21415
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 163492230
91.1%
Uppercase Letter 10256788
 
5.7%
Decimal Number 5744772
 
3.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4018989
70.0%
6 796215
 
13.9%
2 580732
 
10.1%
4 182244
 
3.2%
8 97817
 
1.7%
0 31275
 
0.5%
9 16085
 
0.3%
3 14465
 
0.3%
5 6950
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
C 5128394
50.0%
T 5128394
50.0%
Space Separator
ValueCountFrequency (%)
163492230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 169237002
94.3%
Latin 10256788
 
5.7%

Most frequent character per script

Common
ValueCountFrequency (%)
163492230
96.6%
1 4018989
 
2.4%
6 796215
 
0.5%
2 580732
 
0.3%
4 182244
 
0.1%
8 97817
 
0.1%
0 31275
 
< 0.1%
9 16085
 
< 0.1%
3 14465
 
< 0.1%
5 6950
 
< 0.1%
Latin
ValueCountFrequency (%)
C 5128394
50.0%
T 5128394
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 179493790
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
163492230
91.1%
C 5128394
 
2.9%
T 5128394
 
2.9%
1 4018989
 
2.2%
6 796215
 
0.4%
2 580732
 
0.3%
4 182244
 
0.1%
8 97817
 
0.1%
0 31275
 
< 0.1%
9 16085
 
< 0.1%
Other values (2) 21415
 
< 0.1%

promocion_cabecera
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
0
4977468 
1
 
150926

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5128394
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4977468
97.1%
1 150926
 
2.9%

Length

2023-07-11T22:40:47.952220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T22:40:48.050242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4977468
97.1%
1 150926
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 4977468
97.1%
1 150926
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5128394
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4977468
97.1%
1 150926
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 5128394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4977468
97.1%
1 150926
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5128394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4977468
97.1%
1 150926
 
2.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
0
3955689 
1
1172705 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5128394
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3955689
77.1%
1 1172705
 
22.9%

Length

2023-07-11T22:40:48.130257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T22:40:48.230273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3955689
77.1%
1 1172705
 
22.9%

Most occurring characters

ValueCountFrequency (%)
0 3955689
77.1%
1 1172705
 
22.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5128394
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3955689
77.1%
1 1172705
 
22.9%

Most occurring scripts

ValueCountFrequency (%)
Common 5128394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3955689
77.1%
1 1172705
 
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5128394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3955689
77.1%
1 1172705
 
22.9%

promocion_expositor
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
0
4998423 
1
 
129971

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5128394
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4998423
97.5%
1 129971
 
2.5%

Length

2023-07-11T22:40:48.312286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T22:40:48.409275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4998423
97.5%
1 129971
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 4998423
97.5%
1 129971
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5128394
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4998423
97.5%
1 129971
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 5128394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4998423
97.5%
1 129971
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5128394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4998423
97.5%
1 129971
 
2.5%

promocion_extra_cantidad
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
0
5120929 
1
 
7465

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5128394
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5120929
99.9%
1 7465
 
0.1%

Length

2023-07-11T22:40:48.489290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T22:40:48.587505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5120929
99.9%
1 7465
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 5120929
99.9%
1 7465
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5128394
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5120929
99.9%
1 7465
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5128394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5120929
99.9%
1 7465
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5128394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5120929
99.9%
1 7465
 
0.1%

promocion_folleto
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
0
4861493 
1
 
266901

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5128394
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4861493
94.8%
1 266901
 
5.2%

Length

2023-07-11T22:40:48.666492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T22:40:48.764538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4861493
94.8%
1 266901
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 4861493
94.8%
1 266901
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5128394
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4861493
94.8%
1 266901
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common 5128394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4861493
94.8%
1 266901
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5128394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4861493
94.8%
1 266901
 
5.2%

promocion_isla
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
0
5087672 
1
 
40722

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5128394
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5087672
99.2%
1 40722
 
0.8%

Length

2023-07-11T22:40:48.844554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T22:40:48.946542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5087672
99.2%
1 40722
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 5087672
99.2%
1 40722
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5128394
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5087672
99.2%
1 40722
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5128394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5087672
99.2%
1 40722
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5128394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5087672
99.2%
1 40722
 
0.8%

promocion_multicompra
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
0
5089206 
1
 
39188

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5128394
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5089206
99.2%
1 39188
 
0.8%

Length

2023-07-11T22:40:49.043558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T22:40:49.144607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5089206
99.2%
1 39188
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 5089206
99.2%
1 39188
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5128394
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5089206
99.2%
1 39188
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5128394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5089206
99.2%
1 39188
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5128394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5089206
99.2%
1 39188
 
0.8%

promocion_regalo
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
0
5099520 
1
 
28874

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5128394
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5099520
99.4%
1 28874
 
0.6%

Length

2023-07-11T22:40:49.223591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T22:40:49.322610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5099520
99.4%
1 28874
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 5099520
99.4%
1 28874
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5128394
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5099520
99.4%
1 28874
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 5128394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5099520
99.4%
1 28874
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5128394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5099520
99.4%
1 28874
 
0.6%
Distinct7534
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
2023-07-11T22:40:49.761909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.3016219
Min length1

Characters and Unicode

Total characters22060412
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11,48
2nd row11,48
3rd row11,48
4th row11,48
5th row11,48
ValueCountFrequency (%)
2,76 99182
 
1.9%
2,74 67178
 
1.3%
2,77 57527
 
1.1%
2,78 53705
 
1.0%
3,25 47395
 
0.9%
2,83 46483
 
0.9%
2,73 43998
 
0.9%
2,71 42546
 
0.8%
3,18 40182
 
0.8%
2,25 38812
 
0.8%
Other values (7524) 4591386
89.5%
2023-07-11T22:40:50.344624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 5067513
23.0%
2 2869905
13.0%
1 2616458
11.9%
3 2267998
10.3%
4 1590642
 
7.2%
7 1579306
 
7.2%
5 1512018
 
6.9%
6 1316242
 
6.0%
8 1316220
 
6.0%
9 1181866
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16992899
77.0%
Other Punctuation 5067513
 
23.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2869905
16.9%
1 2616458
15.4%
3 2267998
13.3%
4 1590642
9.4%
7 1579306
9.3%
5 1512018
8.9%
6 1316242
7.7%
8 1316220
7.7%
9 1181866
7.0%
0 742244
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 5067513
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22060412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 5067513
23.0%
2 2869905
13.0%
1 2616458
11.9%
3 2267998
10.3%
4 1590642
 
7.2%
7 1579306
 
7.2%
5 1512018
 
6.9%
6 1316242
 
6.0%
8 1316220
 
6.0%
9 1181866
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22060412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 5067513
23.0%
2 2869905
13.0%
1 2616458
11.9%
3 2267998
10.3%
4 1590642
 
7.2%
7 1579306
 
7.2%
5 1512018
 
6.9%
6 1316242
 
6.0%
8 1316220
 
6.0%
9 1181866
 
5.4%

cod_canal
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
1
2619365 
2
2509029 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5128394
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2619365
51.1%
2 2509029
48.9%

Length

2023-07-11T22:40:50.468699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T22:40:50.571133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2619365
51.1%
2 2509029
48.9%

Most occurring characters

ValueCountFrequency (%)
1 2619365
51.1%
2 2509029
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5128394
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2619365
51.1%
2 2509029
48.9%

Most occurring scripts

ValueCountFrequency (%)
Common 5128394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2619365
51.1%
2 2509029
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5128394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2619365
51.1%
2 2509029
48.9%

cod_provincia
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.55498
Minimum1
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 MiB
2023-07-11T22:40:50.677153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q117
median37
Q391
95-th percentile95
Maximum95
Range94
Interquartile range (IQR)74

Descriptive statistics

Standard deviation31.918183
Coefficient of variation (CV)0.7328251
Kurtosis-1.0633644
Mean43.55498
Median Absolute Deviation (MAD)20
Skewness0.56897541
Sum2.233671 × 108
Variance1018.7704
MonotonicityNot monotonic
2023-07-11T22:40:50.811147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 760358
 
14.8%
91 533064
 
10.4%
46 280369
 
5.5%
29 184985
 
3.6%
50 181904
 
3.5%
41 177107
 
3.5%
48 165783
 
3.2%
3 163717
 
3.2%
7 141135
 
2.8%
33 140177
 
2.7%
Other values (40) 2399795
46.8%
ValueCountFrequency (%)
1 34012
 
0.7%
2 72694
1.4%
3 163717
3.2%
4 52169
 
1.0%
5 10193
 
0.2%
6 71342
1.4%
7 141135
2.8%
8 132377
2.6%
9 55444
 
1.1%
10 75141
1.5%
ValueCountFrequency (%)
95 760358
14.8%
91 533064
10.4%
50 181904
 
3.5%
49 9985
 
0.2%
48 165783
 
3.2%
47 118308
 
2.3%
46 280369
 
5.5%
45 35481
 
0.7%
44 18638
 
0.4%
43 130275
 
2.5%

postal_code
Real number (ℝ)

HIGH CORRELATION 

Distinct403
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24589.853
Minimum0
Maximum50630
Zeros4621
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size39.1 MiB
2023-07-11T22:40:50.948203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3550
Q19973
median28003
Q336210
95-th percentile48640
Maximum50630
Range50630
Interquartile range (IQR)26237

Descriptive statistics

Standard deviation14716.98
Coefficient of variation (CV)0.59849808
Kurtosis-1.2211534
Mean24589.853
Median Absolute Deviation (MAD)15001
Skewness0.17772523
Sum1.2610646 × 1011
Variance2.165895 × 108
MonotonicityNot monotonic
2023-07-11T22:40:51.093237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28220 58631
 
1.1%
28013 57507
 
1.1%
28043 56320
 
1.1%
8940 53075
 
1.0%
10001 50609
 
1.0%
8028 50579
 
1.0%
29004 50156
 
1.0%
3550 48674
 
0.9%
50008 43102
 
0.8%
37005 43037
 
0.8%
Other values (393) 4616704
90.0%
ValueCountFrequency (%)
0 4621
 
0.1%
1008 22695
0.4%
1012 11317
 
0.2%
2002 20615
0.4%
2003 4856
 
0.1%
2005 14669
0.3%
2006 32554
0.6%
2484 6097
 
0.1%
3013 10022
 
0.2%
3176 19327
0.4%
ValueCountFrequency (%)
50630 3511
 
0.1%
50300 14951
 
0.3%
50018 34713
0.7%
50017 16218
 
0.3%
50015 1354
 
< 0.1%
50014 3710
 
0.1%
50009 11481
 
0.2%
50008 43102
0.8%
50007 8226
 
0.2%
50006 1777
 
< 0.1%

sales_surface_sqmeters
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5109.7552
Minimum100
Maximum17600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 MiB
2023-07-11T22:40:51.234264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile400
Q11100
median2500
Q310000
95-th percentile12900
Maximum17600
Range17500
Interquartile range (IQR)8900

Descriptive statistics

Standard deviation4718.8499
Coefficient of variation (CV)0.92349823
Kurtosis-0.96939079
Mean5109.7552
Median Absolute Deviation (MAD)1900
Skewness0.68220932
Sum2.6204838 × 1010
Variance22267545
MonotonicityNot monotonic
2023-07-11T22:40:51.365263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
700 207022
 
4.0%
1600 184325
 
3.6%
800 163661
 
3.2%
300 153071
 
3.0%
900 142486
 
2.8%
2000 140966
 
2.7%
500 136976
 
2.7%
1800 136514
 
2.7%
2500 128949
 
2.5%
2300 124072
 
2.4%
Other values (75) 3610352
70.4%
ValueCountFrequency (%)
100 9571
 
0.2%
200 86479
1.7%
300 153071
3.0%
400 111252
2.2%
500 136976
2.7%
600 100670
2.0%
700 207022
4.0%
800 163661
3.2%
900 142486
2.8%
1000 82950
1.6%
ValueCountFrequency (%)
17600 28222
0.6%
17100 30155
0.6%
16000 29883
0.6%
15900 33011
0.6%
13300 39734
0.8%
13200 29730
0.6%
13000 34536
0.7%
12900 59348
1.2%
12800 34266
0.7%
12500 31752
0.6%

Canal
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
Supermercados
2619365 
Hipermercados
2509029 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters66669122
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSupermercados
2nd rowSupermercados
3rd rowSupermercados
4th rowSupermercados
5th rowSupermercados

Common Values

ValueCountFrequency (%)
Supermercados 2619365
51.1%
Hipermercados 2509029
48.9%

Length

2023-07-11T22:40:51.490322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T22:40:51.594332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
supermercados 2619365
51.1%
hipermercados 2509029
48.9%

Most occurring characters

ValueCountFrequency (%)
e 10256788
15.4%
r 10256788
15.4%
p 5128394
7.7%
m 5128394
7.7%
c 5128394
7.7%
a 5128394
7.7%
d 5128394
7.7%
o 5128394
7.7%
s 5128394
7.7%
S 2619365
 
3.9%
Other values (3) 7637423
11.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 61540728
92.3%
Uppercase Letter 5128394
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10256788
16.7%
r 10256788
16.7%
p 5128394
8.3%
m 5128394
8.3%
c 5128394
8.3%
a 5128394
8.3%
d 5128394
8.3%
o 5128394
8.3%
s 5128394
8.3%
u 2619365
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
S 2619365
51.1%
H 2509029
48.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 66669122
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10256788
15.4%
r 10256788
15.4%
p 5128394
7.7%
m 5128394
7.7%
c 5128394
7.7%
a 5128394
7.7%
d 5128394
7.7%
o 5128394
7.7%
s 5128394
7.7%
S 2619365
 
3.9%
Other values (3) 7637423
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66669122
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10256788
15.4%
r 10256788
15.4%
p 5128394
7.7%
m 5128394
7.7%
c 5128394
7.7%
a 5128394
7.7%
d 5128394
7.7%
o 5128394
7.7%
s 5128394
7.7%
S 2619365
 
3.9%
Other values (3) 7637423
11.5%

Channel
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
Supermarkets
2619365 
Hipermarkets
2509029 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters61540728
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSupermarkets
2nd rowSupermarkets
3rd rowSupermarkets
4th rowSupermarkets
5th rowSupermarkets

Common Values

ValueCountFrequency (%)
Supermarkets 2619365
51.1%
Hipermarkets 2509029
48.9%

Length

2023-07-11T22:40:51.680320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-11T22:40:51.783342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
supermarkets 2619365
51.1%
hipermarkets 2509029
48.9%

Most occurring characters

ValueCountFrequency (%)
e 10256788
16.7%
r 10256788
16.7%
p 5128394
8.3%
m 5128394
8.3%
a 5128394
8.3%
k 5128394
8.3%
t 5128394
8.3%
s 5128394
8.3%
S 2619365
 
4.3%
u 2619365
 
4.3%
Other values (2) 5018058
8.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 56412334
91.7%
Uppercase Letter 5128394
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10256788
18.2%
r 10256788
18.2%
p 5128394
9.1%
m 5128394
9.1%
a 5128394
9.1%
k 5128394
9.1%
t 5128394
9.1%
s 5128394
9.1%
u 2619365
 
4.6%
i 2509029
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
S 2619365
51.1%
H 2509029
48.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 61540728
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10256788
16.7%
r 10256788
16.7%
p 5128394
8.3%
m 5128394
8.3%
a 5128394
8.3%
k 5128394
8.3%
t 5128394
8.3%
s 5128394
8.3%
S 2619365
 
4.3%
u 2619365
 
4.3%
Other values (2) 5018058
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61540728
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10256788
16.7%
r 10256788
16.7%
p 5128394
8.3%
m 5128394
8.3%
a 5128394
8.3%
k 5128394
8.3%
t 5128394
8.3%
s 5128394
8.3%
S 2619365
 
4.3%
u 2619365
 
4.3%
Other values (2) 5018058
8.2%

Provincia
Categorical

HIGH CORRELATION 

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
Area Metropolitana de Madrid
760358 
Area Metropolitana de Barcelona
533064 
Valencia
 
280369
Malaga
 
184985
Zaragoza
 
181904
Other values (45)
3187714 

Length

Max length31
Median length11
Mean length13.018834
Min length4

Characters and Unicode

Total characters66765708
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLa Rioja
2nd rowLa Rioja
3rd rowLa Rioja
4th rowLa Rioja
5th rowLa Rioja

Common Values

ValueCountFrequency (%)
Area Metropolitana de Madrid 760358
 
14.8%
Area Metropolitana de Barcelona 533064
 
10.4%
Valencia 280369
 
5.5%
Malaga 184985
 
3.6%
Zaragoza 181904
 
3.5%
Sevilla 177107
 
3.5%
Vizcaya 165783
 
3.2%
Alicante 163717
 
3.2%
Baleares 141135
 
2.8%
Asturias 140177
 
2.7%
Other values (40) 2399795
46.8%

Length

2023-07-11T22:40:51.883455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
area 1293422
14.1%
de 1293422
14.1%
metropolitana 1293422
14.1%
madrid 820150
 
8.9%
barcelona 665441
 
7.2%
valencia 280369
 
3.0%
malaga 184985
 
2.0%
zaragoza 181904
 
2.0%
sevilla 177107
 
1.9%
vizcaya 165783
 
1.8%
Other values (43) 2847535
30.9%

Most occurring characters

ValueCountFrequency (%)
a 12436620
18.6%
e 6387100
9.6%
r 5878662
 
8.8%
o 4591486
 
6.9%
l 4100034
 
6.1%
4075146
 
6.1%
i 3927137
 
5.9%
d 3765460
 
5.6%
t 3214533
 
4.8%
n 3145205
 
4.7%
Other values (29) 15244325
22.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54780444
82.0%
Uppercase Letter 7910118
 
11.8%
Space Separator 4075146
 
6.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 12436620
22.7%
e 6387100
11.7%
r 5878662
10.7%
o 4591486
 
8.4%
l 4100034
 
7.5%
i 3927137
 
7.2%
d 3765460
 
6.9%
t 3214533
 
5.9%
n 3145205
 
5.7%
c 1662989
 
3.0%
Other values (12) 5671218
10.4%
Uppercase Letter
ValueCountFrequency (%)
M 2426519
30.7%
A 1766384
22.3%
B 933362
 
11.8%
C 600004
 
7.6%
V 564460
 
7.1%
G 349417
 
4.4%
L 255534
 
3.2%
S 252473
 
3.2%
Z 191889
 
2.4%
T 184394
 
2.3%
Other values (6) 385682
 
4.9%
Space Separator
ValueCountFrequency (%)
4075146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 62690562
93.9%
Common 4075146
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 12436620
19.8%
e 6387100
10.2%
r 5878662
9.4%
o 4591486
 
7.3%
l 4100034
 
6.5%
i 3927137
 
6.3%
d 3765460
 
6.0%
t 3214533
 
5.1%
n 3145205
 
5.0%
M 2426519
 
3.9%
Other values (28) 12817806
20.4%
Common
ValueCountFrequency (%)
4075146
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66668952
99.9%
None 96756
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 12436620
18.7%
e 6387100
9.6%
r 5878662
 
8.8%
o 4591486
 
6.9%
l 4100034
 
6.1%
4075146
 
6.1%
i 3927137
 
5.9%
d 3765460
 
5.6%
t 3214533
 
4.8%
n 3145205
 
4.7%
Other values (28) 15147569
22.7%
None
ValueCountFrequency (%)
ñ 96756
100.0%

Comunidad autónoma
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.1 MiB
Cataluña
876786 
Madrid
820150 
Andalucía
741975 
Comunidad Valenciana
543869 
Castilla y León
357993 
Other values (11)
1787621 

Length

Max length20
Median length15
Mean length10.054475
Min length6

Characters and Unicode

Total characters51563309
Distinct characters36
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLa Rioja
2nd rowLa Rioja
3rd rowLa Rioja
4th rowLa Rioja
5th rowLa Rioja

Common Values

ValueCountFrequency (%)
Cataluña 876786
17.1%
Madrid 820150
16.0%
Andalucía 741975
14.5%
Comunidad Valenciana 543869
10.6%
Castilla y León 357993
7.0%
País Vasco 336962
 
6.6%
Aragón 230949
 
4.5%
Castilla-La Mancha 220173
 
4.3%
Galicia 200228
 
3.9%
Extremadura 146483
 
2.9%
Other values (6) 652826
12.7%

Length

2023-07-11T22:40:52.012047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cataluña 876786
12.5%
madrid 820150
11.7%
andalucía 741975
10.6%
comunidad 543869
 
7.8%
valenciana 543869
 
7.8%
castilla 357993
 
5.1%
y 357993
 
5.1%
león 357993
 
5.1%
país 336962
 
4.8%
vasco 336962
 
4.8%
Other values (12) 1716362
24.6%

Most occurring characters

ValueCountFrequency (%)
a 11760615
22.8%
l 3660325
 
7.1%
d 3616496
 
7.0%
i 3301715
 
6.4%
n 3284233
 
6.4%
u 2577252
 
5.0%
c 2171169
 
4.2%
C 2100357
 
4.1%
r 2047847
 
4.0%
1862520
 
3.6%
Other values (26) 15180780
29.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42627522
82.7%
Uppercase Letter 6853094
 
13.3%
Space Separator 1862520
 
3.6%
Dash Punctuation 220173
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 11760615
27.6%
l 3660325
 
8.6%
d 3616496
 
8.5%
i 3301715
 
7.7%
n 3284233
 
7.7%
u 2577252
 
6.0%
c 2171169
 
5.1%
r 2047847
 
4.8%
t 1843148
 
4.3%
s 1673579
 
3.9%
Other values (13) 6691143
15.7%
Uppercase Letter
ValueCountFrequency (%)
C 2100357
30.6%
M 1168285
17.0%
A 1113101
16.2%
V 880831
12.9%
L 623696
 
9.1%
P 336962
 
4.9%
G 200228
 
2.9%
E 146483
 
2.1%
B 141135
 
2.1%
N 96486
 
1.4%
Space Separator
ValueCountFrequency (%)
1862520
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 220173
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 49480616
96.0%
Common 2082693
 
4.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 11760615
23.8%
l 3660325
 
7.4%
d 3616496
 
7.3%
i 3301715
 
6.7%
n 3284233
 
6.6%
u 2577252
 
5.2%
c 2171169
 
4.4%
C 2100357
 
4.2%
r 2047847
 
4.1%
t 1843148
 
3.7%
Other values (24) 13117459
26.5%
Common
ValueCountFrequency (%)
1862520
89.4%
- 220173
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49018644
95.1%
None 2544665
 
4.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 11760615
24.0%
l 3660325
 
7.5%
d 3616496
 
7.4%
i 3301715
 
6.7%
n 3284233
 
6.7%
u 2577252
 
5.3%
c 2171169
 
4.4%
C 2100357
 
4.3%
r 2047847
 
4.2%
1862520
 
3.8%
Other values (23) 12636115
25.8%
None
ValueCountFrequency (%)
í 1078937
42.4%
ñ 876786
34.5%
ó 588942
23.1%

Lat
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.441079
Minimum36.52988
Maximum43.36846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 MiB
2023-07-11T22:40:52.140731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum36.52988
5-th percentile36.71847
Q139.46894
median40.44823
Q341.65419
95-th percentile43.237467
Maximum43.36846
Range6.83858
Interquartile range (IQR)2.18525

Descriptive statistics

Standard deviation1.9437366
Coefficient of variation (CV)0.048063419
Kurtosis-0.73377353
Mean40.441079
Median Absolute Deviation (MAD)1.2036
Skewness-0.39748953
Sum2.0739779 × 108
Variance3.7781118
MonotonicityNot monotonic
2023-07-11T22:40:52.266779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.44823 760358
 
14.8%
41.37591 533064
 
10.4%
39.46894 280369
 
5.5%
36.71847 184985
 
3.6%
41.65183 181904
 
3.5%
37.38788 177107
 
3.5%
43.23746674 165783
 
3.2%
38.3441 163717
 
3.2%
39.57422795 141135
 
2.8%
43.29208774 140177
 
2.7%
Other values (40) 2399795
46.8%
ValueCountFrequency (%)
36.52988 121319
2.4%
36.71847 184985
3.6%
36.84191 52169
 
1.0%
37.17054 95580
1.9%
37.25455 41450
 
0.8%
37.38788 177107
3.5%
37.76908 23041
 
0.4%
37.87064 46324
 
0.9%
37.98308 127962
2.5%
38.3441 163717
3.2%
ValueCountFrequency (%)
43.36846 96756
1.9%
43.29208774 140177
2.7%
43.23746674 165783
3.2%
43.19721408 101536
2.0%
43.14362932 137167
2.7%
43.00951 26359
 
0.5%
42.8346584 34012
 
0.7%
42.66705063 96486
1.9%
42.59917 69636
1.4%
42.43246 49782
 
1.0%

Lon
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.4353536
Minimum-8.65237
Maximum2.9126485
Zeros0
Zeros (%)0.0%
Negative4110473
Negative (%)80.2%
Memory size39.1 MiB
2023-07-11T22:40:52.398778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-8.65237
5-th percentile-6.97272
Q1-4.41965
median-3.16446
Q3-0.37686
95-th percentile2.17001
Maximum2.9126485
Range11.565018
Interquartile range (IQR)4.04279

Descriptive statistics

Standard deviation3.0101011
Coefficient of variation (CV)-1.2360016
Kurtosis-0.84806468
Mean-2.4353536
Median Absolute Deviation (MAD)2.28332
Skewness0.14861423
Sum-12489453
Variance9.0607084
MonotonicityNot monotonic
2023-07-11T22:40:52.533836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.71272 760358
 
14.8%
2.14988 533064
 
10.4%
-0.37686 280369
 
5.5%
-4.41965 184985
 
3.6%
-0.88114 181904
 
3.5%
-6.00196 177107
 
3.5%
-2.852817223 165783
 
3.2%
-0.48043 163717
 
3.2%
2.912648499 141135
 
2.8%
-5.993136553 140177
 
2.7%
Other values (40) 2399795
46.8%
ValueCountFrequency (%)
-8.65237 49782
 
1.0%
-8.4021 96756
1.9%
-7.86627 27331
 
0.5%
-7.5694 26359
 
0.5%
-6.97272 71342
1.4%
-6.94518 41450
 
0.8%
-6.36939 75141
1.5%
-6.29027 121319
2.4%
-6.00196 177107
3.5%
-5.993136553 140177
2.7%
ValueCountFrequency (%)
2.912648499 141135
 
2.8%
2.81873 63817
 
1.2%
2.17001 132377
 
2.6%
2.14988 533064
10.4%
1.25385 130275
 
2.5%
0.62755 17253
 
0.3%
-0.04594 99783
 
1.9%
-0.37686 280369
5.5%
-0.40919 30407
 
0.6%
-0.48043 163717
 
3.2%

TEMP_MINIMA
Real number (ℝ)

HIGH CORRELATION 

Distinct1696
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.30611
Minimum-4.2428571
Maximum25.942857
Zeros0
Zeros (%)0.0%
Negative107968
Negative (%)2.1%
Memory size39.1 MiB
2023-07-11T22:40:52.668923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-4.2428571
5-th percentile1.6
Q16.4285714
median10.4
Q316.514286
95-th percentile21.957143
Maximum25.942857
Range30.185714
Interquartile range (IQR)10.085714

Descriptive statistics

Standard deviation6.3467754
Coefficient of variation (CV)0.56135801
Kurtosis-0.9296542
Mean11.30611
Median Absolute Deviation (MAD)4.9142857
Skewness0.16129249
Sum57982187
Variance40.281558
MonotonicityNot monotonic
2023-07-11T22:40:52.796991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.8 17756
 
0.3%
9.142857143 17067
 
0.3%
14.75714286 15958
 
0.3%
10.11428571 15241
 
0.3%
21.25714286 15226
 
0.3%
16.8 15070
 
0.3%
0.3285714286 14979
 
0.3%
11.7 14928
 
0.3%
7.785714286 14687
 
0.3%
4.342857143 14647
 
0.3%
Other values (1686) 4972835
97.0%
ValueCountFrequency (%)
-4.242857143 50
 
< 0.1%
-4.214285714 40
 
< 0.1%
-3.871428571 35
 
< 0.1%
-3.8 170
 
< 0.1%
-3.685714286 107
 
< 0.1%
-3.585714286 146
 
< 0.1%
-3.5 54
 
< 0.1%
-3.328571429 160
 
< 0.1%
-3.3 1045
< 0.1%
-3.271428571 177
 
< 0.1%
ValueCountFrequency (%)
25.94285714 1263
 
< 0.1%
25.08571429 486
 
< 0.1%
24.7 1974
 
< 0.1%
24.65714286 473
 
< 0.1%
24.52857143 1314
 
< 0.1%
24.48571429 2495
< 0.1%
24.45714286 934
 
< 0.1%
24.41428571 449
 
< 0.1%
24.3 5977
0.1%
24.22857143 206
 
< 0.1%

TEMP_MAXIMA
Real number (ℝ)

HIGH CORRELATION 

Distinct1908
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.710162
Minimum1.9142857
Maximum40.485714
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.1 MiB
2023-07-11T22:40:52.943104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.9142857
5-th percentile9.7142857
Q114.042857
median18.342857
Q325.371429
95-th percentile33.057143
Maximum40.485714
Range38.571429
Interquartile range (IQR)11.328571

Descriptive statistics

Standard deviation7.2630143
Coefficient of variation (CV)0.36849084
Kurtosis-0.69160452
Mean19.710162
Median Absolute Deviation (MAD)5.4571429
Skewness0.42728603
Sum1.0108148 × 108
Variance52.751377
MonotonicityNot monotonic
2023-07-11T22:40:53.084100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.32857143 18807
 
0.4%
11.07142857 17437
 
0.3%
9.928571429 16692
 
0.3%
15.1 15246
 
0.3%
15.78571429 15064
 
0.3%
12 15057
 
0.3%
15.11428571 14899
 
0.3%
11.27142857 14732
 
0.3%
33.58571429 14496
 
0.3%
15.6 14295
 
0.3%
Other values (1898) 4971669
96.9%
ValueCountFrequency (%)
1.914285714 50
 
< 0.1%
1.985714286 54
 
< 0.1%
3.057142857 120
 
< 0.1%
3.542857143 107
 
< 0.1%
3.657142857 317
< 0.1%
3.714285714 177
 
< 0.1%
3.757142857 35
 
< 0.1%
3.928571429 158
 
< 0.1%
3.985714286 125
 
< 0.1%
4 522
< 0.1%
ValueCountFrequency (%)
40.48571429 333
 
< 0.1%
40.05714286 206
 
< 0.1%
39.38571429 1513
< 0.1%
38.65714286 1509
< 0.1%
38.62857143 637
 
< 0.1%
38.44285714 331
 
< 0.1%
38.41428571 1550
< 0.1%
38.38571429 328
 
< 0.1%
38.37142857 327
 
< 0.1%
38.31428571 1611
< 0.1%

TEMP_MEDIA
Real number (ℝ)

HIGH CORRELATION 

Distinct1812
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.250407
Minimum-1.1142857
Maximum32.171429
Zeros0
Zeros (%)0.0%
Negative463
Negative (%)< 0.1%
Memory size39.1 MiB
2023-07-11T22:40:53.233130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.1142857
5-th percentile5.4714286
Q19.9428571
median14.2
Q320.542857
95-th percentile26.814286
Maximum32.171429
Range33.285714
Interquartile range (IQR)10.6

Descriptive statistics

Standard deviation6.7727328
Coefficient of variation (CV)0.44410177
Kurtosis-0.89912829
Mean15.250407
Median Absolute Deviation (MAD)5.2428571
Skewness0.26268868
Sum78210095
Variance45.869909
MonotonicityNot monotonic
2023-07-11T22:40:53.363153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.02857143 30840
 
0.6%
12.14285714 16387
 
0.3%
10.55714286 16089
 
0.3%
8.957142857 15967
 
0.3%
6.928571429 15007
 
0.3%
12.68571429 14924
 
0.3%
12.12857143 14708
 
0.3%
23.15714286 14514
 
0.3%
10.57142857 13999
 
0.3%
21.94285714 13996
 
0.3%
Other values (1802) 4961963
96.8%
ValueCountFrequency (%)
-1.114285714 50
 
< 0.1%
-0.614285714 54
 
< 0.1%
-0.571428571 107
< 0.1%
-0.414285714 35
 
< 0.1%
-0.1 177
< 0.1%
-0.028571429 40
 
< 0.1%
0.1 137
< 0.1%
0.1285714286 120
< 0.1%
0.2428571429 158
< 0.1%
0.3142857143 125
< 0.1%
ValueCountFrequency (%)
32.17142857 333
 
< 0.1%
32.15714286 206
 
< 0.1%
30.92857143 314
 
< 0.1%
30.91428571 331
 
< 0.1%
30.8 1550
< 0.1%
30.7 621
< 0.1%
30.61428571 1416
< 0.1%
30.58571429 694
< 0.1%
30.55714286 205
 
< 0.1%
30.51428571 195
 
< 0.1%

PRECIPITACION
Real number (ℝ)

ZEROS 

Distinct716
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8346999
Minimum0
Maximum36.828571
Zeros592889
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size39.1 MiB
2023-07-11T22:40:53.512179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median0.72857143
Q32.4142857
95-th percentile7.4571429
Maximum36.828571
Range36.828571
Interquartile range (IQR)2.3142857

Descriptive statistics

Standard deviation2.739525
Coefficient of variation (CV)1.4931733
Kurtosis11.098656
Mean1.8346999
Median Absolute Deviation (MAD)0.71428571
Skewness2.744634
Sum9409064
Variance7.5049972
MonotonicityNot monotonic
2023-07-11T22:40:53.657204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 592889
 
11.6%
0.0142857143 163441
 
3.2%
0.0285714286 163147
 
3.2%
0.0428571429 99356
 
1.9%
0.0714285714 88574
 
1.7%
0.0571428571 70663
 
1.4%
0.1285714286 69055
 
1.3%
0.0857142857 63958
 
1.2%
0.1 59313
 
1.2%
0.1571428571 59260
 
1.2%
Other values (706) 3698738
72.1%
ValueCountFrequency (%)
0 592889
11.6%
0.0142857143 163441
 
3.2%
0.0285714286 163147
 
3.2%
0.0428571429 99356
 
1.9%
0.0571428571 70663
 
1.4%
0.0714285714 88574
 
1.7%
0.0857142857 63958
 
1.2%
0.1 59313
 
1.2%
0.1142857143 58775
 
1.1%
0.1285714286 69055
 
1.3%
ValueCountFrequency (%)
36.82857143 490
 
< 0.1%
23.5 446
 
< 0.1%
22.92857143 264
 
< 0.1%
21.7 323
 
< 0.1%
21.6 1135
< 0.1%
21.22857143 818
< 0.1%
20.68571429 1630
< 0.1%
20.61428571 847
< 0.1%
19.9 404
 
< 0.1%
18.87142857 1672
< 0.1%

national_holidays_2021
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
False
4870027 
True
 
258367
ValueCountFrequency (%)
False 4870027
95.0%
True 258367
 
5.0%
2023-07-11T22:40:53.787259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

regional_holidays_2021
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
False
4440046 
True
688348 
ValueCountFrequency (%)
False 4440046
86.6%
True 688348
 
13.4%
2023-07-11T22:40:53.878850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

local_holidays_2021
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
False
3826461 
True
1301933 
ValueCountFrequency (%)
False 3826461
74.6%
True 1301933
 
25.4%
2023-07-11T22:40:53.971055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

national_holidays_2022
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
False
4816426 
True
 
311968
ValueCountFrequency (%)
False 4816426
93.9%
True 311968
 
6.1%
2023-07-11T22:40:54.066178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

regional_holidays_2022
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
False
4505969 
True
622425 
ValueCountFrequency (%)
False 4505969
87.9%
True 622425
 
12.1%
2023-07-11T22:40:54.160844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

local_holidays_2022
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
False
3470237 
True
1658157 
ValueCountFrequency (%)
False 3470237
67.7%
True 1658157
32.3%
2023-07-11T22:40:54.254970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

national_holidays_2023
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
False
4992459 
True
 
135935
ValueCountFrequency (%)
False 4992459
97.3%
True 135935
 
2.7%
2023-07-11T22:40:54.346013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

regional_holidays_2023
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
False
4725430 
True
 
402964
ValueCountFrequency (%)
False 4725430
92.1%
True 402964
 
7.9%
2023-07-11T22:40:54.435036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

local_holidays_2023
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
False
4582860 
True
545534 
ValueCountFrequency (%)
False 4582860
89.4%
True 545534
 
10.6%
2023-07-11T22:40:54.528018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Interactions

2023-07-11T22:39:29.022903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:09.969965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:23.277388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:36.679830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:50.419614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:04.334176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:17.981229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:31.306958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:44.306132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:58.306683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:11.986795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:26.030955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:39.813056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:53.151505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:06.918047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:20.923144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:35.107278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:49.121438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:02.758166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:16.210568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:29.662019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:10.695096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:23.908504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:37.339951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:51.133822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:04.985567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:18.631921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:31.964038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:44.989257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:58.969805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:12.694096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:26.718080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:40.472176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:53.802627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:07.610208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:21.625762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:35.885493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:49.814616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:03.423738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:16.839683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:30.299135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:11.352216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:24.580626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:37.975067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:51.867249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:05.640334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:19.278832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:32.618383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:45.741393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:59.634926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:13.437231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:27.385202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:41.114296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:54.452744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:08.277034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:22.325234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:36.579141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:50.513447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:04.060853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:17.474798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:30.940251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:12.015367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:25.238746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:38.636187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:52.532372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:06.290294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:20.004781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:33.264894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:46.442521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:00.311048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:14.111356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:28.078328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:41.777414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:55.107908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:08.970162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:23.020575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:37.268267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:51.201955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:04.704971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:18.117918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:31.587369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:12.691459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:26.126907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:39.304344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:53.347520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:06.921376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:20.695715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:33.925178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:47.159652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:00.991172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:14.846489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:28.746450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:42.426532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:55.790033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:09.660285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:23.699064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:37.970433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:51.896870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:05.352089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:18.758033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-07-11T22:35:19.991789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:33.397233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:46.733665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:00.910609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:14.709352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:28.038605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:41.050539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:54.835050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:08.605170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:22.630335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:36.281824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-07-11T22:36:15.352609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:28.681499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:41.683657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:55.523176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:09.277292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:23.313465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:36.970949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:50.515302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:04.213916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:17.968908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:32.204233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:46.307650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:00.118765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:13.578180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:26.475439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:40.158442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:21.296028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:34.708471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:48.204416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:02.318519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:16.010875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:29.334204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:42.324771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:56.226304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:09.935414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:23.982581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:37.657074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:51.187426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:04.879153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:18.687041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:32.884993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:38:47.013609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:00.768141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:14.222977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:27.119555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:40.816562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:21.964149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:35:35.367591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-07-11T22:36:02.995347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:16.673526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:29.997129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:42.981891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:56.926431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:10.604536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:24.667706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:37:38.345911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-07-11T22:39:41.449677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-07-11T22:36:17.324061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:36:30.651816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-07-11T22:39:15.543447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-11T22:39:28.378785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-07-11T22:40:54.688237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Unnamed: 0cod_tiendacod_semanacod_productoventas_unidadesventas_valorventas_volumenprecio_real_unidadesprecio_real_volumenprecio_tarifa_unidadesprecio_tarifa_volumencod_provinciapostal_codesales_surface_sqmetersLatLonTEMP_MINIMATEMP_MAXIMATEMP_MEDIAPRECIPITACIONmonthyearseasonSEGMENTMANUFACTURERBRANDPACKAGINGVOLUMEUNITSpromocion_cabecerapromocion_descuentopromocion_expositorpromocion_extra_cantidadpromocion_folletopromocion_islapromocion_multicomprapromocion_regalocod_canalCanalChannelProvinciaComunidad autónomanational_holidays_2021regional_holidays_2021local_holidays_2021national_holidays_2022regional_holidays_2022local_holidays_2022national_holidays_2023regional_holidays_2023local_holidays_2023
Unnamed: 01.0000.7470.0160.0080.0070.0090.0090.0010.0020.0000.002-0.065-0.0260.0100.0210.040-0.008-0.001-0.005-0.0070.0720.0450.0610.0060.0100.0130.0040.0070.0060.0060.0070.0100.0050.0150.0100.0070.0060.0430.0430.0430.1330.0890.0520.0580.0470.0400.0700.0410.0490.0550.057
cod_tienda0.7471.0000.001-0.0010.0110.0170.0130.0070.0030.0070.003-0.051-0.0170.0850.0140.052-0.0090.003-0.004-0.0110.0060.0170.0080.0170.0340.0360.0210.0180.0190.0200.0210.0190.0080.0300.0190.0210.0200.2140.2140.2140.4090.2350.0050.0100.0130.0020.0050.0100.0060.0140.017
cod_semana0.0160.0011.000-0.010-0.021-0.014-0.0180.011-0.0060.009-0.0060.0050.013-0.0140.001-0.010-0.172-0.156-0.1700.0410.5720.9740.7730.0150.0110.0240.0110.0360.0160.0130.0400.0280.0120.0950.0100.0140.0300.0190.0190.0190.0280.0090.3910.6550.8770.3560.6300.8280.3450.6710.766
cod_producto0.008-0.001-0.0101.0000.1640.0450.216-0.2280.078-0.2330.078-0.038-0.009-0.1270.009-0.0050.0120.0090.011-0.0030.0070.0190.0090.7380.3230.4840.1950.2600.1720.0600.0500.0510.0460.0410.0460.0270.0510.1320.1320.1320.0500.0420.0070.0170.0220.0030.0060.0110.0080.0140.019
ventas_unidades0.0070.011-0.0210.1641.0000.7960.811-0.437-0.200-0.419-0.2000.019-0.0230.036-0.0210.0490.0450.0500.049-0.0280.0030.0000.0030.0080.0140.0110.0070.0010.0020.0190.0190.0120.0000.0260.0000.0500.0000.0120.0120.0120.0080.0040.0000.0000.0020.0020.0000.0010.0030.0000.002
ventas_valor0.0090.017-0.0140.0450.7961.0000.9360.1410.2820.1560.2820.026-0.0270.117-0.0310.0600.0410.0450.044-0.0290.0030.0010.0030.0040.0040.0070.0060.0000.0140.0260.0180.0160.0000.0220.0010.0210.0000.0120.0120.0120.0050.0040.0000.0000.0010.0020.0010.0020.0000.0000.001
ventas_volumen0.0090.013-0.0180.2160.8110.9361.0000.0200.3490.0380.349-0.007-0.0230.023-0.0340.0360.0440.0490.047-0.0320.0030.0010.0030.0060.0090.0090.0100.0020.0140.0330.0270.0220.0000.0340.0000.0440.0000.0170.0170.0170.0070.0050.0000.0000.0020.0030.0000.0010.0020.0010.002
precio_real_unidades0.0010.0070.011-0.228-0.4370.1410.0201.0000.7730.9900.7730.014-0.0040.131-0.0100.017-0.012-0.013-0.0130.0020.0050.0060.0050.0260.0170.0490.3380.4620.0560.0000.0230.0030.0000.0070.0020.0050.0360.0290.0290.0290.0080.0060.0000.0020.0020.0030.0040.0070.0030.0060.010
precio_real_volumen0.0020.003-0.0060.078-0.2000.2820.3490.7731.0000.7771.000-0.038-0.002-0.000-0.031-0.0190.0000.0030.001-0.0120.0020.0030.0020.1050.0530.0720.0600.7100.4860.0730.0370.0570.0010.0660.0100.0090.0060.0770.0770.0770.0310.0220.0010.0030.0040.0000.0020.0020.0000.0010.002
precio_tarifa_unidades0.0000.0070.009-0.233-0.4190.1560.0380.9900.7771.0000.7770.016-0.0020.136-0.0110.015-0.009-0.009-0.0090.0010.0030.0060.0030.0240.0170.0460.3260.4060.0780.0090.0130.0070.0000.0150.0020.0030.0340.0350.0350.0350.0100.0070.0010.0020.0030.0030.0040.0060.0020.0050.007
precio_tarifa_volumen0.0020.003-0.0060.078-0.2000.2820.3490.7731.0000.7771.000-0.038-0.002-0.000-0.031-0.0190.0000.0030.001-0.0120.0020.0030.0020.1050.0530.0720.0600.7100.4860.0730.0370.0570.0010.0660.0100.0090.0060.0770.0770.0770.0310.0220.0010.0030.0040.0000.0020.0020.0000.0010.002
cod_provincia-0.065-0.0510.005-0.0380.0190.026-0.0070.014-0.0380.016-0.0381.0000.4330.0580.2080.043-0.100-0.027-0.0610.0140.0050.0130.0050.0290.0620.0880.0300.0270.0260.0200.0200.0200.0110.0130.0190.0290.0190.0540.0540.0541.0000.7000.0050.0120.0140.0030.0050.0090.0030.0060.007
postal_code-0.026-0.0170.013-0.009-0.023-0.027-0.023-0.004-0.002-0.002-0.0020.4331.0000.0570.170-0.267-0.080-0.014-0.0470.0220.0040.0210.0060.0220.0650.0820.0280.0230.0190.0260.0240.0240.0150.0150.0160.0260.0210.1180.1180.1180.9520.6160.0070.0160.0230.0040.0080.0150.0070.0120.015
sales_surface_sqmeters0.0100.085-0.014-0.1270.0360.1170.0230.131-0.0000.136-0.0000.0580.0571.000-0.068-0.0170.0140.0140.014-0.0040.0040.0150.0050.0670.1040.1020.0750.0620.0650.0600.0330.0410.0170.1270.0280.0600.0260.9110.9110.9110.4490.2790.0050.0130.0140.0030.0070.0120.0030.0070.008
Lat0.0210.0140.0010.009-0.021-0.031-0.034-0.010-0.031-0.011-0.0310.2080.170-0.0681.0000.085-0.200-0.236-0.2170.2790.0040.0150.0060.0260.0780.1070.0300.0320.0240.0170.0160.0360.0190.0190.0270.0350.0260.1330.1330.1331.0000.7520.0050.0100.0150.0020.0070.0100.0040.0080.010
Lon0.0400.052-0.010-0.0050.0490.0600.0360.017-0.0190.015-0.0190.043-0.267-0.0170.0851.0000.1100.0220.0620.0040.0040.0160.0060.0270.0960.1150.0320.0310.0290.0240.0260.0230.0090.0340.0230.0260.0190.1020.1020.1021.0000.8400.0060.0130.0180.0020.0060.0100.0050.0090.012
TEMP_MINIMA-0.008-0.009-0.1720.0120.0450.0410.044-0.0120.000-0.0090.000-0.100-0.0800.014-0.2000.1101.0000.9250.979-0.3050.3700.2380.5060.0070.0200.0250.0100.0100.0110.0040.0290.0320.0050.0400.0140.0120.0230.0200.0200.0200.2110.1890.0540.0880.2840.1030.1480.2000.1770.2800.325
TEMP_MAXIMA-0.0010.003-0.1560.0090.0500.0450.049-0.0130.003-0.0090.003-0.027-0.0140.014-0.2360.0220.9251.0000.980-0.4680.3610.2510.5080.0090.0240.0290.0110.0120.0110.0050.0360.0320.0040.0490.0140.0120.0190.0260.0260.0260.2300.1990.0710.1000.2710.0750.1620.2570.1720.2960.336
TEMP_MEDIA-0.005-0.004-0.1700.0110.0490.0440.047-0.0130.001-0.0090.001-0.061-0.0470.014-0.2170.0620.9790.9801.000-0.3860.3850.2440.5220.0090.0230.0280.0100.0110.0110.0040.0330.0320.0040.0470.0130.0150.0220.0260.0260.0260.2220.1930.0750.1260.2900.0990.1780.2350.1740.3010.338
PRECIPITACION-0.007-0.0110.041-0.003-0.028-0.029-0.0320.002-0.0120.001-0.0120.0140.022-0.0040.2790.004-0.305-0.468-0.3861.0000.0980.1440.1040.0040.0120.0140.0060.0040.0060.0020.0070.0090.0010.0110.0040.0030.0020.0200.0200.0200.1530.1320.0300.0420.1030.0640.0700.1140.1660.1740.128
month0.0720.0060.5720.0070.0030.0030.0030.0050.0020.0030.0020.0050.0040.0040.0040.0040.3700.3610.3850.0981.0000.3551.0000.0080.0070.0090.0090.0140.0120.0090.0530.0290.0060.0750.0070.0430.0320.0070.0070.0070.0090.0040.2890.3150.4350.2880.4000.3150.2770.3410.388
year0.0450.0170.9740.0190.0000.0010.0010.0060.0030.0060.0030.0130.0210.0150.0150.0160.2380.2510.2440.1440.3551.0000.2590.0230.0130.0430.0070.0580.0220.0020.0050.0080.0110.0560.0040.0030.0100.0170.0170.0170.0530.0150.2600.4440.6580.1440.4070.6080.3070.6460.723
season0.0610.0080.7730.0090.0030.0030.0030.0050.0020.0030.0020.0050.0060.0050.0060.0060.5060.5080.5220.1041.0000.2591.0000.0110.0080.0130.0060.0210.0160.0050.0370.0220.0050.0540.0060.0180.0200.0030.0030.0030.0140.0060.1800.2190.3840.2370.2310.2930.0920.2510.299
SEGMENT0.0060.0170.0150.7380.0080.0040.0060.0260.1050.0240.1050.0290.0220.0670.0260.0270.0070.0090.0090.0040.0080.0230.0111.0000.3060.4190.2300.2500.1690.0620.0510.0500.0450.0420.0430.0200.0490.1430.1430.1430.0450.0360.0080.0200.0260.0040.0070.0150.0080.0150.019
MANUFACTURER0.0100.0340.0110.3230.0140.0040.0090.0170.0530.0170.0530.0620.0650.1040.0780.0960.0200.0240.0230.0120.0070.0130.0080.3061.0001.0000.2090.2650.1980.0800.1140.0520.0710.0460.0400.0300.0450.1830.1830.1830.1240.1110.0040.0110.0110.0020.0070.0110.0050.0100.012
BRAND0.0130.0360.0240.4840.0110.0070.0090.0490.0720.0460.0720.0880.0820.1020.1070.1150.0250.0290.0280.0140.0090.0430.0130.4191.0001.0000.2560.2700.1710.1050.1390.0580.0770.0660.0760.0370.0690.2120.2120.2120.1020.1080.0160.0300.0400.0060.0080.0170.0170.0310.038
PACKAGING0.0040.0210.0110.1950.0070.0060.0100.3380.0600.3260.0600.0300.0280.0750.0300.0320.0100.0110.0100.0060.0090.0070.0060.2300.2090.2561.0000.6940.2360.0500.0920.0450.0130.0700.0100.0610.0430.0950.0950.0950.0590.0390.0000.0090.0110.0030.0050.0080.0030.0030.005
VOLUME0.0070.0180.0360.2600.0010.0000.0020.4620.7100.4060.7100.0270.0230.0620.0320.0310.0100.0120.0110.0040.0140.0580.0210.2500.2650.2700.6941.0000.2560.0310.0780.0230.0830.0660.0350.0560.0390.1340.1340.1340.0280.0300.0180.0570.0670.0130.0220.0420.0150.0270.033
UNITS0.0060.0190.0160.1720.0020.0140.0140.0560.4860.0780.4860.0260.0190.0650.0240.0290.0110.0110.0110.0060.0120.0220.0160.1690.1980.1710.2360.2561.0000.1290.1000.0940.1290.1180.0290.0460.2590.1600.1600.1600.0420.0290.0070.0150.0190.0030.0100.0200.0060.0110.013
promocion_cabecera0.0060.0200.0130.0600.0190.0260.0330.0000.0730.0090.0730.0200.0260.0600.0170.0240.0040.0050.0040.0020.0090.0020.0050.0620.0800.1050.0500.0310.1291.0000.1350.0260.0180.1390.0150.1070.0370.0500.0500.0500.0470.0300.0010.0030.0030.0010.0010.0040.0000.0010.002
promocion_descuento0.0070.0210.0400.0500.0190.0180.0270.0230.0370.0130.0370.0200.0240.0330.0160.0260.0290.0360.0330.0070.0530.0050.0370.0510.1140.1390.0920.0780.1000.1351.0000.0970.0260.2920.0320.1100.0030.0090.0090.0090.0560.0330.0080.0170.0180.0080.0020.0020.0050.0020.011
promocion_expositor0.0100.0190.0280.0510.0120.0160.0220.0030.0570.0070.0570.0200.0240.0410.0360.0230.0320.0320.0320.0090.0290.0080.0220.0500.0520.0580.0450.0230.0940.0260.0971.0000.0100.0330.0140.1080.0250.0430.0430.0430.0600.0420.0010.0060.0130.0030.0020.0030.0020.0030.007
promocion_extra_cantidad0.0050.0080.0120.0460.0000.0000.0000.0000.0010.0000.0010.0110.0150.0170.0190.0090.0050.0040.0040.0010.0060.0110.0050.0450.0710.0770.0130.0830.1290.0180.0260.0101.0000.0070.0090.0020.0100.0050.0050.0050.0320.0180.0030.0050.0060.0020.0040.0070.0000.0020.000
promocion_folleto0.0150.0300.0950.0410.0260.0220.0340.0070.0660.0150.0660.0130.0150.1270.0190.0340.0400.0490.0470.0110.0750.0560.0540.0420.0460.0660.0700.0660.1180.1390.2920.0330.0071.0000.0200.1150.0060.1120.1120.1120.0700.0410.0120.0150.0020.0070.0180.0430.0210.0300.041
promocion_isla0.0100.0190.0100.0460.0000.0010.0000.0020.0100.0020.0100.0190.0160.0280.0270.0230.0140.0140.0130.0040.0070.0040.0060.0430.0400.0760.0100.0350.0290.0150.0320.0140.0090.0201.0000.0120.0150.0140.0140.0140.0560.0450.0000.0020.0000.0000.0000.0000.0010.0030.003
promocion_multicompra0.0070.0210.0140.0270.0500.0210.0440.0050.0090.0030.0090.0290.0260.0600.0350.0260.0120.0120.0150.0030.0430.0030.0180.0200.0300.0370.0610.0560.0460.1070.1100.1080.0020.1150.0121.0000.0020.0500.0500.0500.0620.0400.0000.0020.0070.0070.0060.0010.0030.0040.002
promocion_regalo0.0060.0200.0300.0510.0000.0000.0000.0360.0060.0340.0060.0190.0210.0260.0260.0190.0230.0190.0220.0020.0320.0100.0200.0490.0450.0690.0430.0390.2590.0370.0030.0250.0100.0060.0150.0021.0000.0120.0120.0120.0530.0350.0050.0090.0160.0020.0060.0050.0040.0020.002
cod_canal0.0430.2140.0190.1320.0120.0120.0170.0290.0770.0350.0770.0540.1180.9110.1330.1020.0200.0260.0260.0200.0070.0170.0030.1430.1830.2120.0950.1340.1600.0500.0090.0430.0050.1120.0140.0500.0121.0001.0001.0000.3290.1850.0040.0100.0120.0020.0050.0090.0010.0050.005
Canal0.0430.2140.0190.1320.0120.0120.0170.0290.0770.0350.0770.0540.1180.9110.1330.1020.0200.0260.0260.0200.0070.0170.0030.1430.1830.2120.0950.1340.1600.0500.0090.0430.0050.1120.0140.0500.0121.0001.0001.0000.3290.1850.0040.0100.0120.0020.0050.0090.0010.0050.005
Channel0.0430.2140.0190.1320.0120.0120.0170.0290.0770.0350.0770.0540.1180.9110.1330.1020.0200.0260.0260.0200.0070.0170.0030.1430.1830.2120.0950.1340.1600.0500.0090.0430.0050.1120.0140.0500.0121.0001.0001.0000.3290.1850.0040.0100.0120.0020.0050.0090.0010.0050.005
Provincia0.1330.4090.0280.0500.0080.0050.0070.0080.0310.0100.0311.0000.9520.4491.0001.0000.2110.2300.2220.1530.0090.0530.0140.0450.1240.1020.0590.0280.0420.0470.0560.0600.0320.0700.0560.0620.0530.3290.3290.3291.0001.0000.0190.0390.0550.0090.0200.0340.0150.0260.032
Comunidad autónoma0.0890.2350.0090.0420.0040.0040.0050.0060.0220.0070.0220.7000.6160.2790.7520.8400.1890.1990.1930.1320.0040.0150.0060.0360.1110.1080.0390.0300.0290.0300.0330.0420.0180.0410.0450.0400.0350.1850.1850.1851.0001.0000.0060.0120.0150.0020.0070.0110.0060.0100.012
national_holidays_20210.0520.0050.3910.0070.0000.0000.0000.0000.0010.0010.0010.0050.0070.0050.0050.0060.0540.0710.0750.0300.2890.2600.1800.0080.0040.0160.0000.0180.0070.0010.0080.0010.0030.0120.0000.0000.0050.0040.0040.0040.0190.0061.0000.1290.0420.0590.0860.1590.0380.0670.079
regional_holidays_20210.0580.0100.6550.0170.0000.0000.0000.0020.0030.0020.0030.0120.0160.0130.0100.0130.0880.1000.1260.0420.3150.4440.2190.0200.0110.0300.0090.0570.0150.0030.0170.0060.0050.0150.0020.0020.0090.0100.0100.0100.0390.0120.1291.0000.5090.1000.1460.2720.0650.1150.136
local_holidays_20210.0470.0130.8770.0220.0020.0010.0020.0020.0040.0030.0040.0140.0230.0140.0150.0180.2840.2710.2900.1030.4350.6580.3840.0260.0110.0400.0110.0670.0190.0030.0180.0130.0060.0020.0000.0070.0160.0120.0120.0120.0550.0150.0420.5091.0000.1480.2170.4030.0960.1700.201
national_holidays_20220.0400.0020.3560.0030.0020.0020.0030.0030.0000.0030.0000.0030.0040.0030.0020.0020.1030.0750.0990.0640.2880.1440.2370.0040.0020.0060.0030.0130.0030.0010.0080.0030.0020.0070.0000.0070.0020.0020.0020.0020.0090.0020.0590.1000.1481.0000.0150.0170.0420.0740.088
regional_holidays_20220.0700.0050.6300.0060.0000.0010.0000.0040.0020.0040.0020.0050.0080.0070.0070.0060.1480.1620.1780.0700.4000.4070.2310.0070.0070.0080.0050.0220.0100.0010.0020.0020.0040.0180.0000.0060.0060.0050.0050.0050.0200.0070.0860.1460.2170.0151.0000.4250.0610.1090.128
local_holidays_20220.0410.0100.8280.0110.0010.0020.0010.0070.0020.0060.0020.0090.0150.0120.0100.0100.2000.2570.2350.1140.3150.6080.2930.0150.0110.0170.0080.0420.0200.0040.0020.0030.0070.0430.0000.0010.0050.0090.0090.0090.0340.0110.1590.2720.4030.0170.4251.0000.1140.2020.238
national_holidays_20230.0490.0060.3450.0080.0030.0000.0020.0030.0000.0020.0000.0030.0070.0030.0040.0050.1770.1720.1740.1660.2770.3070.0920.0080.0050.0170.0030.0150.0060.0000.0050.0020.0000.0210.0010.0030.0040.0010.0010.0010.0150.0060.0380.0650.0960.0420.0610.1141.0000.5650.122
regional_holidays_20230.0550.0140.6710.0140.0000.0000.0010.0060.0010.0050.0010.0060.0120.0070.0080.0090.2800.2960.3010.1740.3410.6460.2510.0150.0100.0310.0030.0270.0110.0010.0020.0030.0020.0300.0030.0040.0020.0050.0050.0050.0260.0100.0670.1150.1700.0740.1090.2020.5651.0000.531
local_holidays_20230.0570.0170.7660.0190.0020.0010.0020.0100.0020.0070.0020.0070.0150.0080.0100.0120.3250.3360.3380.1280.3880.7230.2990.0190.0120.0380.0050.0330.0130.0020.0110.0070.0000.0410.0030.0020.0020.0050.0050.0050.0320.0120.0790.1360.2010.0880.1280.2380.1220.5311.000

Missing values

2023-07-11T22:39:52.869342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-11T22:40:12.651390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0cod_tiendacod_semanacod_productoventas_unidadesventas_valorventas_volumennumero_referenciasprecio_real_unidadesprecio_real_volumenprecio_tarifa_unidadesprecio_tarifa_volumenmonthyearseasonCATEGORYSEGMENTMANUFACTURERBRANDPACKAGINGVOLUMEUNITSpromocion_cabecerapromocion_descuentopromocion_expositorpromocion_extra_cantidadpromocion_folletopromocion_islapromocion_multicomprapromocion_regalofactor_extrapolacioncod_canalcod_provinciapostal_codesales_surface_sqmetersCanalChannelProvinciaComunidad autónomaLatLonTEMP_MINIMATEMP_MAXIMATEMP_MEDIAPRECIPITACIONnational_holidays_2021regional_holidays_2021local_holidays_2021national_holidays_2022regional_holidays_2022local_holidays_2022national_holidays_2023regional_holidays_2023local_holidays_2023
00243422363019.501010.650.330.650.33October2021FallBEEREXTRAMANUFACTURER 2MAN 2 - BRAND 1CAN330ML1CT0000000011,48126260031500SupermercadosSupermarketsLa RiojaLa Rioja42.27474-2.517085.67142912.2857148.81.285714NNNNNNNNN
112434223921.82110.910.330.910.33October2021FallBEEREXTRAMANUFACTURER 2MAN 2 - BRAND 1CRISTAL BOTTLE330ML1CT0000000011,48126260031500SupermercadosSupermarketsLa RiojaLa Rioja42.27474-2.517085.67142912.2857148.81.285714NNNNNNNNN
2224342247210.32415.161.985.161.98October2021FallBEEREXTRAMANUFACTURER 2MAN 2 - BRAND 2CRISTAL BOTTLE330ML6CT0000000011,48126260031500SupermercadosSupermarketsLa RiojaLa Rioja42.27474-2.517085.67142912.2857148.81.285714NNNNNNNNN
3324342258314.97614.991.984.991.98October2021FallBEEREXTRAMANUFACTURER 4MAN 4 - BRAND 1CRISTAL BOTTLE330ML6CT0000000011,48126260031500SupermercadosSupermarketsLa RiojaLa Rioja42.27474-2.517085.67142912.2857148.81.285714NNNNNNNNN
4424342264210.88415.441.985.441.98October2021FallBEEREXTRAMANUFACTURER 4MAN 4 - BRAND 1CRISTAL BOTTLE330ML6CT0000000011,48126260031500SupermercadosSupermarketsLa RiojaLa Rioja42.27474-2.517085.67142912.2857148.81.285714NNNNNNNNN
552434226622.32111.160.331.160.33October2021FallBEEREXTRAMANUFACTURER 2MAN 2 - OTHER BRANDSCRISTAL BOTTLE330ML1CT0000000011,48126260031500SupermercadosSupermarketsLa RiojaLa Rioja42.27474-2.517085.67142912.2857148.81.285714NNNNNNNNN
662434227087.92310.990.330.990.33October2021FallBEEREXTRAMANUFACTURER 3MAN 3 - BRAND 3CRISTAL BOTTLE330ML1CT1000000011,48126260031500SupermercadosSupermarketsLa RiojaLa Rioja42.27474-2.517085.67142912.2857148.81.285714NNNNNNNNN
772434227165.941210.991.980.991.98October2021FallBEEREXTRAMANUFACTURER 3MAN 3 - BRAND 3CRISTAL BOTTLE330ML6CT0000000011,48126260031500SupermercadosSupermarketsLa RiojaLa Rioja42.27474-2.517085.67142912.2857148.81.285714NNNNNNNNN
8824342298149.66510.690.330.690.33October2021FallBEEREXTRAMANUFACTURER 3MAN 3 - BRAND 4CAN330ML1CT0000000011,48126260031500SupermercadosSupermarketsLa RiojaLa Rioja42.27474-2.517085.67142912.2857148.81.285714NNNNNNNNN
9924342302517.50813.501.503.501.50October2021FallBEEREXTRAMANUFACTURER 1MAN 1 - BRAND 1CRISTAL BOTTLE250ML6CT0000000011,48126260031500SupermercadosSupermarketsLa RiojaLa Rioja42.27474-2.517085.67142912.2857148.81.285714NNNNNNNNN
Unnamed: 0cod_tiendacod_semanacod_productoventas_unidadesventas_valorventas_volumennumero_referenciasprecio_real_unidadesprecio_real_volumenprecio_tarifa_unidadesprecio_tarifa_volumenmonthyearseasonCATEGORYSEGMENTMANUFACTURERBRANDPACKAGINGVOLUMEUNITSpromocion_cabecerapromocion_descuentopromocion_expositorpromocion_extra_cantidadpromocion_folletopromocion_islapromocion_multicomprapromocion_regalofactor_extrapolacioncod_canalcod_provinciapostal_codesales_surface_sqmetersCanalChannelProvinciaComunidad autónomaLatLonTEMP_MINIMATEMP_MAXIMATEMP_MEDIAPRECIPITACIONnational_holidays_2021regional_holidays_2021local_holidays_2021national_holidays_2022regional_holidays_2022local_holidays_2022national_holidays_2023regional_holidays_2023local_holidays_2023
51283845128384526476012117.43710.830.330.830.33November2021FallBEERIMPORT PREMIUMOTHER MANUFACTURERSOTHER MAN - OTHER BRANDSCAN330ML1CT000000001,8929180294500HipermercadosHipermarketsArea Metropolitana de BarcelonaCataluña41.375912.149887.65714314.11428610.4428571.242857NNNNNYNNN
512838551283855264760611.09111.090.501.090.50November2021FallBEERIMPORT PREMIUMOTHER MANUFACTURERSOTHER MAN - OTHER BRANDSCAN500ML1CT000000001,8929180294500HipermercadosHipermarketsArea Metropolitana de BarcelonaCataluña41.375912.149887.65714314.11428610.4428571.242857NNNNNYNNN
5128386512838652647607311.37513.791.503.791.50November2021FallBEERIMPORT PREMIUMMANUFACTURER 3MAN 3 - OTHER BRANDSCRISTAL BOTTLE250ML6CT000000001,8929180294500HipermercadosHipermarketsArea Metropolitana de BarcelonaCataluña41.375912.149887.65714314.11428610.4428571.242857NNNNNYNNN
51283875128387526476122416.56810.690.330.690.33November2021FallBEERIMPORT PREMIUMMANUFACTURER 3MAN 3 - OTHER BRANDSCAN330ML1CT000000001,8929180294500HipermercadosHipermarketsArea Metropolitana de BarcelonaCataluña41.375912.149887.65714314.11428610.4428571.242857NNNNNYNNN
51283885128388526476202289.762814.081.265.051.26November2021FallBEERIMPORT PREMIUMOTHER MANUFACTURERSOTHER MAN - OTHER BRANDSCRISTAL BOTTLE210ML6CT110000001,8929180294500HipermercadosHipermarketsArea Metropolitana de BarcelonaCataluña41.375912.149887.65714314.11428610.4428571.242857NNNNNYNNN
5128389512838952647625100127.003611.270.361.270.36November2021FallBEERIMPORT PREMIUMOTHER MANUFACTURERSOTHER MAN - OTHER BRANDSCRISTAL BOTTLE355ML1CT000000001,8929180294500HipermercadosHipermarketsArea Metropolitana de BarcelonaCataluña41.375912.149887.65714314.11428610.4428571.242857NNNNNYNNN
51283905128390526476332049.001012.450.502.450.50November2021FallBEERIMPORT PREMIUMOTHER MANUFACTURERSOTHER MAN - OTHER BRANDSCRISTAL BOTTLE500ML1CT000000001,8929180294500HipermercadosHipermarketsArea Metropolitana de BarcelonaCataluña41.375912.149887.65714314.11428610.4428571.242857NNNNNYNNN
5128391512839152647652637.501816.253.006.253.00November2021FallBEERIMPORT PREMIUMMANUFACTURER 2MAN 2 - BRAND 3CRISTAL BOTTLE250ML12CT000000001,8929180294500HipermercadosHipermarketsArea Metropolitana de BarcelonaCataluña41.375912.149887.65714314.11428610.4428571.242857NNNNNYNNN
51283925128392526476542385.103513.701.503.701.50November2021FallBEERIMPORT PREMIUMMANUFACTURER 2MAN 2 - BRAND 3CRISTAL BOTTLE250ML6CT000000001,8929180294500HipermercadosHipermarketsArea Metropolitana de BarcelonaCataluña41.375912.149887.65714314.11428610.4428571.242857NNNNNYNNN
51283935128393526476554655.201511.200.331.200.33November2021FallBEERIMPORT PREMIUMMANUFACTURER 2MAN 2 - BRAND 3CRISTAL BOTTLE330ML1CT000000001,8929180294500HipermercadosHipermarketsArea Metropolitana de BarcelonaCataluña41.375912.149887.65714314.11428610.4428571.242857NNNNNYNNN